{ "pdf_info": [ { "para_blocks": [ { "bbox": [ 89, 78, 201, 95 ], "type": "title", "angle": 0, "index": 0, "lines": [ { "bbox": [ 85, 77, 203, 98 ], "spans": [ { "bbox": [ 85, 77, 203, 98 ], "type": "text", "content": "1 Introduction", "score": 1.0 } ] } ] }, { "bbox": [ 86, 106, 508, 543 ], "type": "text", "angle": 0, "index": 1, "lines": [ { "bbox": [ 111, 107, 506, 123 ], "spans": [ { "bbox": [ 111, 107, 506, 123 ], "type": "text", "content": "In laparoscopic surgery, high-frequency electroknife, ultrasound knife and other", "score": 1.0 } ] }, { "bbox": [ 88, 131, 506, 147 ], "spans": [ { "bbox": [ 88, 131, 506, 147 ], "type": "text", "content": "energy instruments are widely used for tissue cutting, coagulation and separation. The", "score": 1.0 } ] }, { "bbox": [ 88, 155, 506, 169 ], "spans": [ { "bbox": [ 88, 155, 506, 169 ], "type": "text", "content": "moment these devices come into contact with biological tissues, they convert", "score": 1.0 } ] }, { "bbox": [ 89, 179, 506, 193 ], "spans": [ { "bbox": [ 89, 179, 506, 193 ], "type": "text", "content": "electrical or mechanical energy into heat, causing the intracellular fluid to boil,", "score": 1.0 } ] }, { "bbox": [ 87, 200, 509, 218 ], "spans": [ { "bbox": [ 87, 200, 509, 218 ], "type": "text", "content": "vaporize, and then burst, releasing a mixed aerosol containing water vapor, cell debris,", "score": 1.0 } ] }, { "bbox": [ 88, 224, 506, 240 ], "spans": [ { "bbox": [ 88, 224, 506, 240 ], "type": "text", "content": "carbonized particles and harmful chemicals, known as \"surgical smoke\". while the", "score": 1.0 } ] }, { "bbox": [ 89, 248, 506, 263 ], "spans": [ { "bbox": [ 89, 248, 506, 263 ], "type": "text", "content": "relatively closed and humid environment in the abdominal cavity (relative humidity is", "score": 1.0 } ] }, { "bbox": [ 88, 271, 506, 287 ], "spans": [ { "bbox": [ 88, 271, 506, 287 ], "type": "text", "content": "often close to 100%)The temperature difference between the lens and the endoscope", "score": 1.0 } ] }, { "bbox": [ 88, 295, 506, 310 ], "spans": [ { "bbox": [ 88, 295, 506, 310 ], "type": "text", "content": "lens can easily lead to condensation on the lens surface to form water mist. This visual", "score": 1.0 } ] }, { "bbox": [ 88, 317, 505, 334 ], "spans": [ { "bbox": [ 88, 317, 505, 334 ], "type": "text", "content": "degradation, which is composed of surgical fumes and lens water mist, is highly", "score": 1.0 } ] }, { "bbox": [ 89, 342, 505, 356 ], "spans": [ { "bbox": [ 89, 342, 505, 356 ], "type": "text", "content": "dynamic and complex. It not only reduces the contrast and clarity of the image and", "score": 1.0 } ] }, { "bbox": [ 89, 365, 506, 379 ], "spans": [ { "bbox": [ 89, 365, 506, 379 ], "type": "text", "content": "obscures key anatomical structures such as blood vessels and bile ducts in the", "score": 1.0 } ] }, { "bbox": [ 88, 388, 506, 403 ], "spans": [ { "bbox": [ 88, 388, 506, 403 ], "type": "text", "content": "gallbladder triangle, but can also lead to color distortion and affect doctors' judgment", "score": 1.0 } ] }, { "bbox": [ 89, 412, 506, 426 ], "spans": [ { "bbox": [ 89, 412, 506, 426 ], "type": "text", "content": "of tissue activity. In severe cases, the field of view is completely obstructed, and", "score": 1.0 } ] }, { "bbox": [ 88, 433, 506, 452 ], "spans": [ { "bbox": [ 88, 433, 506, 452 ], "type": "text", "content": "doctors are forced to interrupt the operation, repeatedly pulling out the lens for wiping", "score": 1.0 } ] }, { "bbox": [ 88, 459, 505, 474 ], "spans": [ { "bbox": [ 88, 459, 505, 474 ], "type": "text", "content": "or waiting for the smoke to dissipate. According to statistics, this not only", "score": 1.0 } ] }, { "bbox": [ 88, 481, 506, 497 ], "spans": [ { "bbox": [ 88, 481, 506, 497 ], "type": "text", "content": "significantly prolongs the operation time and increases the risk of anesthesia, but can", "score": 1.0 } ] }, { "bbox": [ 88, 505, 506, 520 ], "spans": [ { "bbox": [ 88, 505, 506, 520 ], "type": "text", "content": "also lead to serious complications such as vascular or organ damage due to blind", "score": 1.0 } ] }, { "bbox": [ 87, 528, 139, 543 ], "spans": [ { "bbox": [ 87, 528, 139, 543 ], "type": "text", "content": "operation.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 551, 506, 755 ], "type": "text", "angle": 0, "index": 2, "lines": [ { "bbox": [ 113, 551, 506, 567 ], "spans": [ { "bbox": [ 113, 551, 506, 567 ], "type": "text", "content": "At present, clinical practice mainly relies on physical means to remove smoke,", "score": 1.0 } ] }, { "bbox": [ 88, 575, 506, 591 ], "spans": [ { "bbox": [ 88, 575, 506, 591 ], "type": "text", "content": "including passive smoke extraction by opening the valve on the trocar, or using an", "score": 1.0 } ] }, { "bbox": [ 88, 597, 506, 615 ], "spans": [ { "bbox": [ 88, 597, 506, 615 ], "type": "text", "content": "active smoking device with a special smoking pipe or electric knife with smoking", "score": 1.0 } ] }, { "bbox": [ 88, 621, 506, 638 ], "spans": [ { "bbox": [ 88, 621, 506, 638 ], "type": "text", "content": "function, or Preoperative lens anti-fog treatment with iodophor or anti-fog oil to wipe", "score": 1.0 } ] }, { "bbox": [ 88, 646, 505, 660 ], "spans": [ { "bbox": [ 88, 646, 505, 660 ], "type": "text", "content": "the lens preoperatively. However, these methods often have problems such as manual", "score": 1.0 } ] }, { "bbox": [ 88, 669, 506, 683 ], "spans": [ { "bbox": [ 88, 669, 506, 683 ], "type": "text", "content": "operation by doctors and short effective time for defogging. In view of the limitations", "score": 1.0 } ] }, { "bbox": [ 88, 691, 505, 708 ], "spans": [ { "bbox": [ 88, 691, 505, 708 ], "type": "text", "content": "of physical means, computer vision-based image dehazing technology (Dehazing)", "score": 1.0 } ] }, { "bbox": [ 88, 714, 506, 731 ], "spans": [ { "bbox": [ 88, 714, 506, 731 ], "type": "text", "content": "came into being. Through algorithm post-processing, clear fog-free images are", "score": 1.0 } ] }, { "bbox": [ 87, 739, 506, 753 ], "spans": [ { "bbox": [ 87, 739, 506, 753 ], "type": "text", "content": "restored from degraded foggy images in real time, so as to achieve \"virtual smoke", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 0 }, { "para_blocks": [ { "bbox": [ 86, 77, 506, 138 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 88, 76, 506, 92 ], "spans": [ { "bbox": [ 88, 76, 506, 92 ], "type": "text", "content": "exhaust\". This can not only ensure a clear field of view, but also avoid the interference", "score": 1.0 } ] }, { "bbox": [ 88, 100, 506, 116 ], "spans": [ { "bbox": [ 88, 100, 506, 116 ], "type": "text", "content": "of physical smoke exhaust on pneumoperitoneal pressure, which is an important", "score": 1.0 } ] }, { "bbox": [ 86, 122, 440, 139 ], "spans": [ { "bbox": [ 86, 122, 440, 139 ], "type": "text", "content": "research direction of smart medicine and computer-aided surgery (CAS).", "score": 1.0 } ] } ] }, { "bbox": [ 86, 147, 508, 327 ], "type": "text", "angle": 0, "index": 1, "lines": [ { "bbox": [ 112, 146, 506, 162 ], "spans": [ { "bbox": [ 112, 146, 506, 162 ], "type": "text", "content": "However, in the face of drastic changes from trace fog to heavy scorched smoke,", "score": 1.0 } ] }, { "bbox": [ 88, 170, 506, 185 ], "spans": [ { "bbox": [ 88, 170, 506, 185 ], "type": "text", "content": "the network model with a single weight is often difficult to take into account all", "score": 1.0 } ] }, { "bbox": [ 88, 194, 506, 209 ], "spans": [ { "bbox": [ 88, 194, 506, 209 ], "type": "text", "content": "scenarios: it is easy to over-dehaze in light fog and cause color distortion, while under", "score": 1.0 } ] }, { "bbox": [ 88, 217, 506, 232 ], "spans": [ { "bbox": [ 88, 217, 506, 232 ], "type": "text", "content": "heavy fog, residual noise may remain. To this end, an adaptive dehazing network", "score": 1.0 } ] }, { "bbox": [ 88, 241, 505, 255 ], "spans": [ { "bbox": [ 88, 241, 505, 255 ], "type": "text", "content": "(Yun-Trans) based on dynamic Mixture of Experts (MoE) is further designed. The", "score": 1.0 } ] }, { "bbox": [ 86, 263, 506, 280 ], "spans": [ { "bbox": [ 86, 263, 506, 280 ], "type": "text", "content": "network introduces \"fog concentration perception\" and \"dynamic weight generation\"", "score": 1.0 } ] }, { "bbox": [ 88, 287, 506, 302 ], "spans": [ { "bbox": [ 88, 287, 506, 302 ], "type": "text", "content": "mechanisms, aiming to achieve accurate matching and adaptive processing of", "score": 1.0 } ] }, { "bbox": [ 89, 311, 258, 326 ], "spans": [ { "bbox": [ 89, 311, 258, 326 ], "type": "text", "content": "different surgical smoke scenarios.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 336, 168, 353 ], "type": "title", "angle": 0, "index": 2, "lines": [ { "bbox": [ 84, 333, 170, 355 ], "spans": [ { "bbox": [ 84, 333, 170, 355 ], "type": "text", "content": "2 Method", "score": 1.0 } ] } ] }, { "bbox": [ 86, 364, 344, 380 ], "type": "title", "angle": 0, "index": 3, "lines": [ { "bbox": [ 87, 364, 344, 380 ], "spans": [ { "bbox": [ 87, 364, 344, 380 ], "type": "text", "content": "2.1 Construction of laparoscopic fog image model", "score": 1.0 } ] } ] }, { "bbox": [ 86, 388, 506, 425 ], "type": "title", "angle": 0, "index": 4, "lines": [ { "bbox": [ 87, 387, 506, 404 ], "spans": [ { "bbox": [ 87, 387, 506, 404 ], "type": "text", "content": "2.1.1 Microscopic physical properties and scattering theory of laparoscopic", "score": 1.0 } ] }, { "bbox": [ 87, 412, 133, 427 ], "spans": [ { "bbox": [ 87, 412, 133, 427 ], "type": "text", "content": "aerosols", "score": 1.0 } ] } ] }, { "bbox": [ 86, 435, 507, 544 ], "type": "text", "angle": 0, "index": 5, "lines": [ { "bbox": [ 112, 434, 506, 450 ], "spans": [ { "bbox": [ 112, 434, 506, 450 ], "type": "text", "content": "Laparoscopic surgery is performed in a relatively enclosed, tiny space filled with", "score": 1.0 } ] }, { "bbox": [ 88, 458, 506, 475 ], "spans": [ { "bbox": [ 88, 458, 506, 475 ], "type": "text", "content": "carbon dioxide pneumoperitoneum, an environment with significant optical features", "score": 1.0 } ] }, { "bbox": [ 89, 482, 505, 496 ], "spans": [ { "bbox": [ 89, 482, 505, 496 ], "type": "text", "content": "that render the standard ASM model ineffective. Therefore, we cannot simply follow", "score": 1.0 } ] }, { "bbox": [ 89, 505, 506, 520 ], "spans": [ { "bbox": [ 89, 505, 192, 520 ], "type": "text", "content": "the standard formula", "score": 1.0 }, { "bbox": [ 192, 505, 280, 518 ], "type": "inline_equation", "content": "I = J t + A ( 1 - t )", "score": 0.7387 }, { "bbox": [ 280, 505, 506, 520 ], "type": "text", "content": ", but need to build a new model that includes", "score": 1.0 } ] }, { "bbox": [ 88, 528, 439, 544 ], "spans": [ { "bbox": [ 88, 528, 439, 544 ], "type": "text", "content": "the light source geometry factor and the bipass transmission mechanism.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 552, 508, 684 ], "type": "text", "angle": 0, "index": 6, "lines": [ { "bbox": [ 112, 551, 506, 567 ], "spans": [ { "bbox": [ 112, 551, 506, 567 ], "type": "text", "content": "To quantify the interaction between light and surgical fumes, the microphysical", "score": 1.0 } ] }, { "bbox": [ 86, 574, 506, 592 ], "spans": [ { "bbox": [ 86, 574, 506, 592 ], "type": "text", "content": "properties of suspended particles were first analyzed. Surgical fume is a complex", "score": 1.0 } ] }, { "bbox": [ 88, 598, 506, 614 ], "spans": [ { "bbox": [ 88, 598, 506, 614 ], "type": "text", "content": "multi-dispersion aerosol system, which mainly includes smoke generated by", "score": 1.0 } ] }, { "bbox": [ 88, 621, 506, 638 ], "spans": [ { "bbox": [ 88, 621, 506, 638 ], "type": "text", "content": "high-frequency electric knife and ultrasound knife and condensate produced by lens", "score": 1.0 } ] }, { "bbox": [ 88, 645, 506, 661 ], "spans": [ { "bbox": [ 88, 645, 506, 661 ], "type": "text", "content": "condensation on the surface, and its optical scattering behavior is described by Mie", "score": 1.0 } ] }, { "bbox": [ 87, 666, 130, 687 ], "spans": [ { "bbox": [ 87, 666, 130, 687 ], "type": "text", "content": "Theory.", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 1 }, { "para_blocks": [ { "type": "image", "bbox": [ 116, 78, 477, 282 ], "blocks": [ { "bbox": [ 116, 78, 477, 282 ], "lines": [ { "bbox": [ 116, 78, 477, 282 ], "spans": [ { "bbox": [ 116, 78, 477, 282 ], "type": "image", "image_path": "5aec9da24c65cacab2f3fe65633f988d4788e2a94da9e4ae848e00d8e1acef18.jpg" } ] } ], "index": 0, "angle": 0, "type": "image_body" }, { "bbox": [ 144, 296, 448, 309 ], "lines": [ { "bbox": [ 146, 295, 448, 309 ], "spans": [ { "bbox": [ 146, 295, 448, 309 ], "type": "text", "content": "Fig.1 Schematic diagram of laparoscopic surgery mist scattering model", "score": 1.0 } ] } ], "index": 1, "angle": 0, "type": "image_caption" } ], "index": 0 }, { "bbox": [ 86, 318, 507, 449 ], "type": "text", "angle": 0, "index": 2, "lines": [ { "bbox": [ 111, 317, 506, 333 ], "spans": [ { "bbox": [ 111, 317, 506, 333 ], "type": "text", "content": "For single spherical particles such as electrocutor smoke and ultrasonic knife", "score": 1.0 } ] }, { "bbox": [ 86, 339, 506, 359 ], "spans": [ { "bbox": [ 86, 339, 506, 359 ], "type": "text", "content": "water mist, their scattering behavior is determined by the scattering", "score": 1.0 } ] }, { "bbox": [ 87, 364, 506, 380 ], "spans": [ { "bbox": [ 87, 364, 157, 380 ], "type": "text", "content": "cross-section", "score": 1.0 }, { "bbox": [ 157, 368, 178, 379 ], "type": "inline_equation", "content": "\\sigma _ { s c a }", "score": 0.8769 }, { "bbox": [ 179, 364, 506, 380 ], "type": "text", "content": "and the scattering phase function ?(?) . Electric knife smoke", "score": 1.0 } ] }, { "bbox": [ 88, 388, 506, 404 ], "spans": [ { "bbox": [ 88, 388, 506, 404 ], "type": "text", "content": "follows the Rayleigh scattering region, i.e., when the radius of the particles is much", "score": 1.0 } ] }, { "bbox": [ 88, 411, 506, 427 ], "spans": [ { "bbox": [ 88, 411, 506, 427 ], "type": "text", "content": "smaller than the wavelength of light ( ? ≪ ? ), the scattered light", "score": 1.0 } ] }, { "bbox": [ 88, 434, 461, 451 ], "spans": [ { "bbox": [ 88, 434, 132, 451 ], "type": "text", "content": "intensity", "score": 1.0 }, { "bbox": [ 133, 436, 152, 449 ], "type": "inline_equation", "content": "I _ { s c a }", "score": 0.882 }, { "bbox": [ 152, 434, 461, 451 ], "type": "text", "content": "is inversely proportional to the fourth power of the wavelength:", "score": 1.0 } ] } ] }, { "bbox": [ 231, 454, 362, 484 ], "type": "interline_equation", "angle": 0, "lines": [ { "bbox": [ 231, 454, 362, 484 ], "spans": [ { "bbox": [ 231, 454, 362, 484 ], "type": "interline_equation", "content": "I _ {s c a} (\\theta) \\propto \\frac {1}{\\lambda^ {4}} (1 + \\cos^ {2} \\theta)", "image_path": "b907f03ab58a21e9fba8ea311e1e4f65ce1fa7bae469af171450ab20a5994f9c.jpg" } ] } ], "index": 3 }, { "bbox": [ 86, 490, 508, 622 ], "type": "text", "angle": 0, "index": 4, "lines": [ { "bbox": [ 112, 488, 506, 505 ], "spans": [ { "bbox": [ 112, 488, 506, 505 ], "type": "text", "content": "This means that blue light (short wavelengths) is scattered the most strongly,", "score": 1.0 } ] }, { "bbox": [ 88, 513, 506, 528 ], "spans": [ { "bbox": [ 88, 513, 506, 528 ], "type": "text", "content": "while red light (long wavelengths) is the most penetrating. This explains why the red", "score": 1.0 } ] }, { "bbox": [ 88, 536, 506, 551 ], "spans": [ { "bbox": [ 88, 536, 506, 551 ], "type": "text", "content": "tissue (blood vessels, muscles) in the abdominal cavity tends to be clearer than the", "score": 1.0 } ] }, { "bbox": [ 88, 560, 506, 575 ], "spans": [ { "bbox": [ 88, 560, 506, 575 ], "type": "text", "content": "blue object (some instrument markings) in the smoke produced by the electroknife.", "score": 1.0 } ] }, { "bbox": [ 88, 583, 506, 598 ], "spans": [ { "bbox": [ 88, 583, 506, 598 ], "type": "text", "content": "For the dehaze model, it means that transmittance ?(?) is a function of wavelength ? ,", "score": 1.0 } ] }, { "bbox": [ 89, 605, 188, 620 ], "spans": [ { "bbox": [ 89, 605, 182, 620 ], "type": "inline_equation", "content": "t ( x , \\lambda ) = e ^ { - \\beta ( \\lambda ) d ( x ) }", "score": 0.9008 }, { "bbox": [ 182, 607, 188, 620 ], "type": "text", "content": ".", "score": 1.0 } ] } ] }, { "bbox": [ 86, 629, 508, 761 ], "type": "text", "angle": 0, "index": 5, "lines": [ { "bbox": [ 112, 629, 506, 645 ], "spans": [ { "bbox": [ 112, 629, 506, 645 ], "type": "text", "content": "In the Mie scattering region formed by ultrasonic knife water mist, when the", "score": 1.0 } ] }, { "bbox": [ 86, 652, 506, 669 ], "spans": [ { "bbox": [ 86, 652, 506, 669 ], "type": "text", "content": "radius of particles is close to or greater than the wavelength of light, the dependence", "score": 1.0 } ] }, { "bbox": [ 88, 676, 508, 693 ], "spans": [ { "bbox": [ 88, 676, 253, 693 ], "type": "text", "content": "of the scattering cross-section", "score": 1.0 }, { "bbox": [ 254, 680, 274, 691 ], "type": "inline_equation", "content": "\\sigma _ { s c a }", "score": 0.8587 }, { "bbox": [ 274, 676, 508, 693 ], "type": "text", "content": "on the wavelength is weakened, which is", "score": 1.0 } ] }, { "bbox": [ 88, 700, 508, 716 ], "spans": [ { "bbox": [ 88, 700, 508, 716 ], "type": "text", "content": "approximately constant. At this point, all colors of light are scattered in equal amounts,", "score": 1.0 } ] }, { "bbox": [ 88, 722, 506, 740 ], "spans": [ { "bbox": [ 88, 722, 506, 740 ], "type": "text", "content": "causing the smoke to appear white. Mie scattering has strong forward scattering", "score": 1.0 } ] }, { "bbox": [ 88, 745, 506, 761 ], "spans": [ { "bbox": [ 88, 745, 506, 761 ], "type": "text", "content": "characteristics, that is, most of the light is scattered forward, but for coaxial", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 2 }, { "para_blocks": [ { "bbox": [ 86, 77, 506, 116 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 88, 75, 506, 93 ], "spans": [ { "bbox": [ 88, 75, 506, 93 ], "type": "text", "content": "illumination imaging systems, we pay more attention to backscattering", "score": 1.0 } ] }, { "bbox": [ 89, 99, 406, 116 ], "spans": [ { "bbox": [ 89, 100, 143, 113 ], "type": "inline_equation", "content": "( \\theta \\approx 1 8 0 ^ { \\circ } )", "score": 0.7432 }, { "bbox": [ 143, 99, 406, 116 ], "type": "text", "content": ", which directly enters the lens to form a light curtain.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 123, 508, 208 ], "type": "text", "angle": 0, "index": 1, "lines": [ { "bbox": [ 112, 123, 506, 139 ], "spans": [ { "bbox": [ 112, 123, 506, 139 ], "type": "text", "content": "In actual surgery, the medium in the pneumoperitoneum is often a mixture of the", "score": 1.0 } ] }, { "bbox": [ 88, 147, 506, 163 ], "spans": [ { "bbox": [ 88, 147, 481, 163 ], "type": "text", "content": "above particles. Assuming that the number density of the class ? particles is", "score": 1.0 }, { "bbox": [ 481, 148, 495, 161 ], "type": "inline_equation", "content": "N _ { i }", "score": 0.8833 }, { "bbox": [ 496, 147, 506, 163 ], "type": "text", "content": ",", "score": 1.0 } ] }, { "bbox": [ 86, 169, 508, 187 ], "spans": [ { "bbox": [ 86, 169, 236, 187 ], "type": "text", "content": "the scattering cross-section is", "score": 1.0 }, { "bbox": [ 236, 173, 262, 185 ], "type": "inline_equation", "content": "\\sigma _ { s c a , i }", "score": 0.9095 }, { "bbox": [ 262, 169, 445, 187 ], "type": "text", "content": ", and the absorption cross-section is", "score": 1.0 }, { "bbox": [ 445, 173, 473, 185 ], "type": "inline_equation", "content": "\\sigma _ { a b s , i }", "score": 0.9071 }, { "bbox": [ 473, 169, 508, 187 ], "type": "text", "content": ", then", "score": 1.0 } ] }, { "bbox": [ 88, 193, 345, 208 ], "spans": [ { "bbox": [ 88, 193, 235, 208 ], "type": "text", "content": "the total extinction coefficient", "score": 1.0 }, { "bbox": [ 235, 195, 257, 208 ], "type": "inline_equation", "content": "\\beta _ { e x t }", "score": 0.912 }, { "bbox": [ 257, 193, 345, 208 ], "type": "text", "content": "of the medium is:", "score": 1.0 } ] } ] }, { "bbox": [ 180, 218, 412, 253 ], "type": "interline_equation", "angle": 0, "lines": [ { "bbox": [ 180, 218, 412, 253 ], "spans": [ { "bbox": [ 180, 218, 412, 253 ], "type": "interline_equation", "content": "\\beta_ {e x t} (\\lambda , x) = \\sum_ {i} N _ {i} (x) \\cdot (\\sigma_ {s c a, i} (\\lambda) + \\sigma_ {a b s, i} (\\lambda))", "image_path": "5a7636ad17f67c55d352228805384f424bafd1288488c92e0dee458bc0b6c2af.jpg" } ] } ], "index": 2 }, { "bbox": [ 86, 264, 508, 490 ], "type": "text", "angle": 0, "index": 3, "lines": [ { "bbox": [ 112, 263, 505, 279 ], "spans": [ { "bbox": [ 112, 263, 505, 279 ], "type": "text", "content": "Since intra-abdominal smoke is mainly scattered, the absorption is usually", "score": 1.0 } ] }, { "bbox": [ 88, 287, 506, 302 ], "spans": [ { "bbox": [ 88, 287, 506, 302 ], "type": "text", "content": "negligible (unless there is a large amount of carbonized black smoke), so the", "score": 1.0 } ] }, { "bbox": [ 88, 309, 506, 327 ], "spans": [ { "bbox": [ 88, 309, 506, 327 ], "type": "text", "content": "extinction coefficient is roughly considered to be equal to the scattering", "score": 1.0 } ] }, { "bbox": [ 88, 333, 506, 350 ], "spans": [ { "bbox": [ 88, 333, 142, 350 ], "type": "text", "content": "coefficient", "score": 1.0 }, { "bbox": [ 142, 334, 163, 348 ], "type": "inline_equation", "content": "\\beta _ { s c a }", "score": 0.9014 }, { "bbox": [ 164, 333, 506, 350 ], "type": "text", "content": ". It should be pointed out that due to the dynamic nature of the surgical", "score": 1.0 } ] }, { "bbox": [ 88, 357, 506, 372 ], "spans": [ { "bbox": [ 88, 357, 140, 372 ], "type": "text", "content": "operation,", "score": 1.0 }, { "bbox": [ 141, 358, 168, 371 ], "type": "inline_equation", "content": "N _ { i } ( x )", "score": 0.9056 }, { "bbox": [ 169, 357, 506, 372 ], "type": "text", "content": "it is a function of time ? and spatial position ? . This is the physical", "score": 1.0 } ] }, { "bbox": [ 88, 380, 506, 396 ], "spans": [ { "bbox": [ 88, 380, 506, 396 ], "type": "text", "content": "motivation for the urgent need to introduce a \"dynamic expert mechanism\" for", "score": 1.0 } ] }, { "bbox": [ 88, 404, 505, 419 ], "spans": [ { "bbox": [ 88, 404, 505, 419 ], "type": "text", "content": "laparoscopic dehazing algorithms: the network needs to dynamically infer whether the", "score": 1.0 } ] }, { "bbox": [ 88, 428, 505, 443 ], "spans": [ { "bbox": [ 88, 428, 505, 443 ], "type": "text", "content": "medium is dominant Rayleigh scattering (wavelength compensation) or Mie", "score": 1.0 } ] }, { "bbox": [ 88, 451, 506, 466 ], "spans": [ { "bbox": [ 88, 451, 506, 466 ], "type": "text", "content": "scattering (contrast enhancement) based on the current image characteristics, so as to", "score": 1.0 } ] }, { "bbox": [ 89, 474, 246, 490 ], "spans": [ { "bbox": [ 89, 474, 246, 490 ], "type": "text", "content": "call different weight parameters.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 497, 456, 513 ], "type": "title", "angle": 0, "index": 4, "lines": [ { "bbox": [ 86, 496, 456, 513 ], "spans": [ { "bbox": [ 86, 496, 456, 513 ], "type": "text", "content": "2.1.2 Mathematical derivation of laparoscopic enhanced imaging model", "score": 1.0 } ] } ] }, { "bbox": [ 86, 521, 507, 583 ], "type": "text", "angle": 0, "index": 5, "lines": [ { "bbox": [ 112, 520, 506, 536 ], "spans": [ { "bbox": [ 112, 520, 506, 536 ], "type": "text", "content": "Based on the physical analysis described above, we will derive a complete", "score": 1.0 } ] }, { "bbox": [ 86, 543, 506, 560 ], "spans": [ { "bbox": [ 86, 543, 506, 560 ], "type": "text", "content": "mathematical model describing the laparoscopic imaging process. The model consists", "score": 1.0 } ] }, { "bbox": [ 88, 566, 458, 583 ], "spans": [ { "bbox": [ 88, 566, 458, 583 ], "type": "text", "content": "of two parts: the Direct Attenuation Term and the Backscatter/Airlight Term.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 592, 261, 606 ], "type": "title", "angle": 0, "index": 6, "lines": [ { "bbox": [ 88, 592, 260, 605 ], "spans": [ { "bbox": [ 88, 592, 260, 605 ], "type": "text", "content": "A. Light source irradiance model", "score": 1.0 } ] } ] }, { "bbox": [ 86, 614, 508, 771 ], "type": "text", "angle": 0, "index": 7, "lines": [ { "bbox": [ 112, 614, 506, 629 ], "spans": [ { "bbox": [ 112, 614, 506, 629 ], "type": "text", "content": "Suppose the endoscopic light source is a point light source located at the", "score": 1.0 } ] }, { "bbox": [ 88, 638, 505, 653 ], "spans": [ { "bbox": [ 88, 638, 123, 653 ], "type": "text", "content": "origin", "score": 1.0 }, { "bbox": [ 124, 638, 157, 651 ], "type": "inline_equation", "content": "( 0 , 0 , 0 )", "score": 0.5341 }, { "bbox": [ 158, 638, 422, 653 ], "type": "text", "content": ", and its luminous intensity angular distribution is", "score": 1.0 }, { "bbox": [ 423, 638, 457, 652 ], "type": "inline_equation", "content": "I _ { s r c } ( \\theta )", "score": 0.917 }, { "bbox": [ 458, 638, 505, 653 ], "type": "text", "content": "(usually", "score": 1.0 } ] }, { "bbox": [ 88, 661, 506, 677 ], "spans": [ { "bbox": [ 88, 661, 506, 677 ], "type": "text", "content": "approximate to the Lambertian source or Gaussian distribution). Light travels through", "score": 1.0 } ] }, { "bbox": [ 86, 684, 506, 699 ], "spans": [ { "bbox": [ 86, 684, 506, 699 ], "type": "text", "content": "a space filled with scattering media. For any point ? in space, the distance from the", "score": 1.0 } ] }, { "bbox": [ 86, 708, 506, 723 ], "spans": [ { "bbox": [ 86, 708, 506, 723 ], "type": "text", "content": "light source is ? , and the angle between the light direction and the optical axis is", "score": 1.0 } ] }, { "bbox": [ 88, 730, 506, 746 ], "spans": [ { "bbox": [ 88, 732, 98, 745 ], "type": "inline_equation", "content": "\\phi", "score": 0.651 }, { "bbox": [ 99, 730, 506, 746 ], "type": "text", "content": ". The irradiance ?(?) of the arrival point ? is affected by two factors: Geometric", "score": 1.0 } ] }, { "bbox": [ 88, 754, 427, 769 ], "spans": [ { "bbox": [ 88, 755, 387, 769 ], "type": "text", "content": "Spreading: the energy decays with the square of the distance,", "score": 1.0 }, { "bbox": [ 387, 754, 423, 768 ], "type": "inline_equation", "content": "\\propto 1 / r ^ { 2 }", "score": 0.9041 }, { "bbox": [ 424, 755, 427, 769 ], "type": "text", "content": ".", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 3 }, { "para_blocks": [ { "bbox": [ 86, 77, 506, 114 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 110, 75, 506, 93 ], "spans": [ { "bbox": [ 110, 75, 506, 93 ], "type": "text", "content": "Medium Extinction: Energy decays with the path exponential,according", "score": 1.0 } ] }, { "bbox": [ 88, 100, 459, 115 ], "spans": [ { "bbox": [ 88, 100, 459, 115 ], "type": "text", "content": "to the Beer-Lambert Law. Therefore, the incident irradiance at the point ? is:", "score": 1.0 } ] } ] }, { "bbox": [ 221, 120, 371, 149 ], "type": "interline_equation", "angle": 0, "lines": [ { "bbox": [ 221, 120, 371, 149 ], "spans": [ { "bbox": [ 221, 120, 371, 149 ], "type": "interline_equation", "content": "E _ {i n} (P) = \\frac {I _ {s r c} (\\phi)}{r ^ {2}} e ^ {- \\int_ {0} ^ {r} \\beta_ {e x t} (s) d s}", "image_path": "4a1ae12f39668db64e5dbb351b40c2a9d325cd369eb2d4f8c2445fc8068d7b46.jpg" } ] } ], "index": 1 }, { "bbox": [ 86, 154, 506, 192 ], "type": "text", "angle": 0, "index": 2, "lines": [ { "bbox": [ 111, 153, 506, 171 ], "spans": [ { "bbox": [ 111, 153, 506, 171 ], "type": "text", "content": "To simplify the model for easy calculation, assuming that the medium is locally", "score": 1.0 } ] }, { "bbox": [ 88, 178, 370, 194 ], "spans": [ { "bbox": [ 88, 178, 370, 194 ], "type": "text", "content": "uniform in the optical path (with a coefficient of ? ), then:", "score": 1.0 } ] } ] }, { "bbox": [ 241, 197, 352, 227 ], "type": "interline_equation", "angle": 0, "lines": [ { "bbox": [ 241, 197, 352, 227 ], "spans": [ { "bbox": [ 241, 197, 352, 227 ], "type": "interline_equation", "content": "E _ {i n} (P) = \\frac {I _ {s r c} (\\phi)}{r ^ {2}} e ^ {- \\beta r}", "image_path": "56b3ff1a78cb9b21e620ee4df12358f048d25f3aad6812efe0b35c9c905272d1.jpg" } ] } ], "index": 3 }, { "bbox": [ 86, 232, 287, 247 ], "type": "title", "angle": 0, "index": 4, "lines": [ { "bbox": [ 88, 232, 287, 248 ], "spans": [ { "bbox": [ 88, 232, 287, 248 ], "type": "text", "content": "B. Direct signal attenuation term ? ?", "score": 1.0 } ] } ] }, { "bbox": [ 86, 255, 508, 419 ], "type": "text", "angle": 0, "index": 5, "lines": [ { "bbox": [ 113, 255, 506, 270 ], "spans": [ { "bbox": [ 113, 255, 506, 270 ], "type": "text", "content": "This is the signal light that we want to recover to carry tissue information. When", "score": 1.0 } ] }, { "bbox": [ 87, 281, 509, 298 ], "spans": [ { "bbox": [ 87, 281, 316, 298 ], "type": "text", "content": "light hits the surface of the tissue (at a position", "score": 1.0 }, { "bbox": [ 317, 283, 336, 297 ], "type": "inline_equation", "content": "\\mathrm { P _ { o b j } }", "score": 0.8934 }, { "bbox": [ 337, 281, 509, 298 ], "type": "text", "content": ", at a distance ?(?) from the lens),", "score": 1.0 } ] }, { "bbox": [ 88, 309, 506, 326 ], "spans": [ { "bbox": [ 88, 309, 423, 326 ], "type": "text", "content": "the tissue is reflected. Let the surface reflectance of the tissue be", "score": 1.0 }, { "bbox": [ 423, 312, 454, 324 ], "type": "inline_equation", "content": "{ \\bf \\Xi } ( { \\bf \\Lambda } \\rho ( x )", "score": 0.6067 }, { "bbox": [ 455, 309, 506, 326 ], "type": "text", "content": ", the core", "score": 1.0 } ] }, { "bbox": [ 88, 334, 506, 349 ], "spans": [ { "bbox": [ 88, 334, 506, 349 ], "type": "text", "content": "component of the potentially clear image ?(?) ). The reflected light returns to the", "score": 1.0 } ] }, { "bbox": [ 88, 357, 506, 373 ], "spans": [ { "bbox": [ 88, 357, 447, 373 ], "type": "text", "content": "camera from the surface of the tissue, again passing through the distance", "score": 1.0 }, { "bbox": [ 448, 358, 471, 371 ], "type": "inline_equation", "content": "d ( x )", "score": 0.5084 }, { "bbox": [ 471, 357, 506, 373 ], "type": "text", "content": "of the", "score": 1.0 } ] }, { "bbox": [ 88, 380, 506, 396 ], "spans": [ { "bbox": [ 88, 380, 506, 396 ], "type": "text", "content": "medium attenuation. Therefore, the intensity of direct reflected light ?(?) received", "score": 1.0 } ] }, { "bbox": [ 88, 404, 271, 419 ], "spans": [ { "bbox": [ 88, 404, 271, 419 ], "type": "text", "content": "by the camera sensor at the pixel ? is:", "score": 1.0 } ] } ] }, { "bbox": [ 196, 428, 395, 464 ], "type": "interline_equation", "angle": 0, "lines": [ { "bbox": [ 196, 428, 395, 464 ], "spans": [ { "bbox": [ 196, 428, 395, 464 ], "type": "interline_equation", "content": "D (x) = \\left(\\frac {I _ {0}}{d (x) ^ {2}} e ^ {- \\beta d (x)}\\right) \\cdot \\rho (x) \\cdot e ^ {- \\beta d (x)}", "image_path": "1df8681e060089ecc94ef9235f52b9ba97c46e9eef2837e8e55dd7e90168d2ca.jpg" } ] } ], "index": 6 }, { "bbox": [ 86, 473, 506, 543 ], "type": "text", "angle": 0, "index": 7, "lines": [ { "bbox": [ 111, 472, 508, 498 ], "spans": [ { "bbox": [ 111, 474, 147, 493 ], "type": "text", "content": "where", "score": 1.0 }, { "bbox": [ 150, 473, 220, 496 ], "type": "inline_equation", "content": "\\left( \\frac { I _ { 0 } } { d ( x ) ^ { 2 } } e ^ { - \\beta d ( x ) } \\right)", "score": 0.9567 }, { "bbox": [ 220, 472, 378, 498 ], "type": "text", "content": "is the incident illuminance,", "score": 1.0 }, { "bbox": [ 379, 478, 402, 491 ], "type": "inline_equation", "content": "\\rho ( x )", "score": 0.6843 }, { "bbox": [ 403, 472, 508, 498 ], "type": "text", "content": "surface reflectance,", "score": 1.0 } ] }, { "bbox": [ 85, 502, 509, 523 ], "spans": [ { "bbox": [ 85, 502, 111, 523 ], "type": "text", "content": "and", "score": 1.0 }, { "bbox": [ 111, 504, 146, 518 ], "type": "inline_equation", "content": "e ^ { - \\beta d ( x ) }", "score": 0.8985 }, { "bbox": [ 146, 502, 509, 523 ], "type": "text", "content": "return path attenuation. After merging the same terms, we obtain a", "score": 1.0 } ] }, { "bbox": [ 87, 528, 334, 545 ], "spans": [ { "bbox": [ 87, 528, 334, 545 ], "type": "text", "content": "two-way attenuation model unique to laparoscopy:", "score": 1.0 } ] } ] }, { "bbox": [ 238, 546, 355, 579 ], "type": "interline_equation", "angle": 0, "lines": [ { "bbox": [ 238, 546, 355, 579 ], "spans": [ { "bbox": [ 238, 546, 355, 579 ], "type": "interline_equation", "content": "D (x) = \\frac {I _ {0} \\rho (x)}{d (x) ^ {2}} e ^ {- 2 \\beta d (x)}", "image_path": "e1426d7f35a70472255ae5afd4d75f00faa02ef0f20befb2a56237f1c3a03949.jpg" } ] } ], "index": 8 }, { "bbox": [ 86, 582, 506, 715 ], "type": "text", "angle": 0, "index": 9, "lines": [ { "bbox": [ 111, 582, 506, 598 ], "spans": [ { "bbox": [ 111, 582, 428, 598 ], "type": "text", "content": "The signal term in the standard atmospheric scattering model is", "score": 1.0 }, { "bbox": [ 429, 582, 486, 597 ], "type": "inline_equation", "content": "J ( x ) e ^ { - \\beta d ( x ) }", "score": 0.9253 }, { "bbox": [ 486, 582, 506, 598 ], "type": "text", "content": ". In", "score": 1.0 } ] }, { "bbox": [ 88, 607, 506, 622 ], "spans": [ { "bbox": [ 88, 607, 506, 622 ], "type": "text", "content": "contrast, the laparoscopic model has one e more factor (two-way attenuation) and a", "score": 1.0 } ] }, { "bbox": [ 88, 629, 506, 645 ], "spans": [ { "bbox": [ 88, 629, 170, 645 ], "type": "text", "content": "geometric factor", "score": 1.0 }, { "bbox": [ 171, 629, 213, 644 ], "type": "inline_equation", "content": "1 / d ( x ) ^ { 2 }", "score": 0.9292 }, { "bbox": [ 213, 629, 424, 645 ], "type": "text", "content": ". This shows that with the increase of depth", "score": 1.0 }, { "bbox": [ 424, 630, 448, 644 ], "type": "inline_equation", "content": "d ( x )", "score": 0.5524 }, { "bbox": [ 449, 629, 506, 645 ], "type": "text", "content": ", the signal", "score": 1.0 } ] }, { "bbox": [ 88, 653, 505, 669 ], "spans": [ { "bbox": [ 88, 653, 505, 669 ], "type": "text", "content": "intensity of laparoscopic images decays much faster than that of outdoor haze images.", "score": 1.0 } ] }, { "bbox": [ 88, 675, 506, 692 ], "spans": [ { "bbox": [ 88, 675, 506, 692 ], "type": "text", "content": "The deep tissue is not only obscured by smoke, but also extremely dark due to", "score": 1.0 } ] }, { "bbox": [ 87, 698, 173, 716 ], "spans": [ { "bbox": [ 87, 698, 173, 716 ], "type": "text", "content": "insufficient light.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 724, 506, 761 ], "type": "text", "angle": 0, "index": 10, "lines": [ { "bbox": [ 111, 721, 506, 740 ], "spans": [ { "bbox": [ 111, 721, 506, 740 ], "type": "text", "content": "This explains the current common phenomenon in surgery: under heavy smoke,", "score": 1.0 } ] }, { "bbox": [ 88, 746, 506, 762 ], "spans": [ { "bbox": [ 88, 746, 506, 762 ], "type": "text", "content": "doctors often do not see deep structures (such as the gallbladder triangle) at all, which", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 4 }, { "para_blocks": [ { "bbox": [ 86, 77, 506, 114 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 86, 76, 506, 92 ], "spans": [ { "bbox": [ 86, 76, 506, 92 ], "type": "text", "content": "is not only a problem of reduced contrast, but also a sharp deterioration of the", "score": 1.0 } ] }, { "bbox": [ 88, 99, 224, 116 ], "spans": [ { "bbox": [ 88, 99, 224, 116 ], "type": "text", "content": "signal-to-noise ratio (SNR).", "score": 1.0 } ] } ] }, { "bbox": [ 86, 123, 431, 139 ], "type": "title", "angle": 0, "index": 1, "lines": [ { "bbox": [ 89, 123, 430, 139 ], "spans": [ { "bbox": [ 89, 123, 430, 139 ], "type": "text", "content": "C. Integral derivation of the backscattered light curtain term ?(?)", "score": 1.0 } ] } ] }, { "bbox": [ 86, 147, 507, 232 ], "type": "text", "angle": 0, "index": 2, "lines": [ { "bbox": [ 111, 146, 506, 163 ], "spans": [ { "bbox": [ 111, 146, 506, 163 ], "type": "text", "content": "This is the main cause of the image \"whitening\" and \"fogging\", corresponding to", "score": 1.0 } ] }, { "bbox": [ 88, 170, 506, 186 ], "spans": [ { "bbox": [ 88, 170, 506, 186 ], "type": "text", "content": "the atmospheric light term ?(1 − ?) in the Standard Model. In laparoscopy, the light", "score": 1.0 } ] }, { "bbox": [ 88, 193, 506, 210 ], "spans": [ { "bbox": [ 88, 193, 506, 210 ], "type": "text", "content": "volume highly coincides with the observation cone due to the coaxial light source", "score": 1.0 } ] }, { "bbox": [ 86, 215, 409, 235 ], "spans": [ { "bbox": [ 86, 215, 409, 235 ], "type": "text", "content": "with the camera, which results in extremely severe backscattering.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 241, 508, 326 ], "type": "text", "angle": 0, "index": 3, "lines": [ { "bbox": [ 112, 240, 506, 255 ], "spans": [ { "bbox": [ 112, 240, 506, 255 ], "type": "text", "content": "Consider the line of sight that emanates from the camera center and passes", "score": 1.0 } ] }, { "bbox": [ 88, 263, 506, 280 ], "spans": [ { "bbox": [ 88, 263, 506, 280 ], "type": "text", "content": "through the pixels ?. In this line of sight, smoke particles within every microparticle", "score": 1.0 } ] }, { "bbox": [ 88, 286, 507, 302 ], "spans": [ { "bbox": [ 88, 286, 260, 302 ], "type": "text", "content": "volume ?? from distance ? = 0 to", "score": 1.0 }, { "bbox": [ 260, 288, 305, 300 ], "type": "inline_equation", "content": "z = d ( x )", "score": 0.6089 }, { "bbox": [ 305, 286, 507, 302 ], "type": "text", "content": "are receiving light and scattering part of", "score": 1.0 } ] }, { "bbox": [ 88, 311, 227, 325 ], "spans": [ { "bbox": [ 88, 311, 227, 325 ], "type": "text", "content": "the light back to the camera.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 333, 508, 497 ], "type": "text", "angle": 0, "index": 4, "lines": [ { "bbox": [ 112, 333, 504, 349 ], "spans": [ { "bbox": [ 112, 333, 468, 349 ], "type": "text", "content": "At depth ?, the light intensity received by the thin layer of smoke ?? is", "score": 1.0 }, { "bbox": [ 468, 334, 504, 347 ], "type": "inline_equation", "content": "E ( z ) =", "score": 0.7763 } ] }, { "bbox": [ 83, 355, 509, 381 ], "spans": [ { "bbox": [ 83, 355, 509, 381 ], "type": "text", "content": "?02 ?−?? . The scattering intensity per unit volume is determined by the ? scattering", "score": 1.0 }, { "bbox": [ 88, 358, 121, 379 ], "type": "inline_equation", "content": "\\frac { I _ { 0 } } { z ^ { 2 } } e ^ { - \\beta z }", "score": 0.9248 }, { "bbox": [ 442, 362, 453, 375 ], "type": "inline_equation", "content": "\\beta", "score": 0.8134 } ] }, { "bbox": [ 88, 388, 506, 404 ], "spans": [ { "bbox": [ 88, 388, 381, 404 ], "type": "text", "content": "coefficient and the backscatter probability (phase function", "score": 1.0 }, { "bbox": [ 382, 389, 417, 402 ], "type": "inline_equation", "content": "P ( \\pi ) ~ )", "score": 0.5321 }, { "bbox": [ 417, 388, 506, 404 ], "type": "text", "content": ". This part of the", "score": 1.0 } ] }, { "bbox": [ 88, 412, 506, 426 ], "spans": [ { "bbox": [ 88, 412, 506, 426 ], "type": "text", "content": "scattered light is transmitted back to the camera and needs to pass through the", "score": 1.0 } ] }, { "bbox": [ 88, 433, 506, 449 ], "spans": [ { "bbox": [ 88, 433, 506, 449 ], "type": "text", "content": "attenuation e of the distance ? again. Therefore, the total backscattered light", "score": 1.0 } ] }, { "bbox": [ 88, 458, 506, 473 ], "spans": [ { "bbox": [ 88, 458, 132, 473 ], "type": "text", "content": "intensity", "score": 1.0 }, { "bbox": [ 133, 459, 157, 472 ], "type": "inline_equation", "content": "B ( x )", "score": 0.5423 }, { "bbox": [ 157, 458, 506, 473 ], "type": "text", "content": "is the integral of the scattering contribution of all microelements on the", "score": 1.0 } ] }, { "bbox": [ 87, 481, 152, 498 ], "spans": [ { "bbox": [ 87, 481, 152, 498 ], "type": "text", "content": "line of sight:", "score": 1.0 } ] } ] }, { "bbox": [ 194, 506, 402, 542 ], "type": "interline_equation", "angle": 0, "lines": [ { "bbox": [ 194, 506, 402, 542 ], "spans": [ { "bbox": [ 194, 506, 402, 542 ], "type": "interline_equation", "content": "B (x) = \\int_ {z _ {m i n}} ^ {d (x)} \\left(\\frac {I _ {0}}{z ^ {2}} e ^ {- \\beta z}\\right) \\cdot \\beta P (\\pi) \\cdot e ^ {- \\beta z} d z", "image_path": "b20e72d0ec47bb4b10d4f497ef253ae0922813018c1bca2a906d3f7a7d426a81.jpg" } ] } ], "index": 5 }, { "bbox": [ 86, 552, 507, 591 ], "type": "text", "angle": 0, "index": 6, "lines": [ { "bbox": [ 111, 551, 507, 567 ], "spans": [ { "bbox": [ 111, 551, 507, 567 ], "type": "text", "content": "Assuming that the scattering medium is uniform and the phase function is", "score": 1.0 } ] }, { "bbox": [ 88, 575, 375, 591 ], "spans": [ { "bbox": [ 88, 575, 149, 591 ], "type": "text", "content": "constant (let", "score": 1.0 }, { "bbox": [ 150, 576, 217, 588 ], "type": "inline_equation", "content": "k = I _ { 0 } \\beta P ( \\pi ) \\rangle", "score": 0.9131 }, { "bbox": [ 217, 575, 375, 591 ], "type": "text", "content": "), the integration is simplified to:", "score": 1.0 } ] } ] }, { "bbox": [ 235, 599, 362, 636 ], "type": "interline_equation", "angle": 0, "lines": [ { "bbox": [ 235, 599, 362, 636 ], "spans": [ { "bbox": [ 235, 599, 362, 636 ], "type": "interline_equation", "content": "B (x) = k \\int_ {z _ {m i n}} ^ {d (x)} \\frac {e ^ {- 2 \\beta z}}{z ^ {2}} d z", "image_path": "a51abf77e730295c492862f6e8c94d3eebe4096814fae6ac1f7b13bf3ae28ca1.jpg" } ] } ], "index": 7 }, { "bbox": [ 86, 645, 507, 731 ], "type": "text", "angle": 0, "index": 8, "lines": [ { "bbox": [ 112, 645, 506, 661 ], "spans": [ { "bbox": [ 112, 645, 506, 661 ], "type": "text", "content": "This integral term reveals a key characteristic of laparoscopic fog images: due to", "score": 1.0 } ] }, { "bbox": [ 88, 667, 506, 684 ], "spans": [ { "bbox": [ 88, 668, 236, 684 ], "type": "text", "content": "the presence of a denominator", "score": 1.0 }, { "bbox": [ 236, 667, 249, 682 ], "type": "inline_equation", "content": "z ^ { 2 }", "score": 0.8681 }, { "bbox": [ 249, 668, 506, 684 ], "type": "text", "content": ", the main contribution of scattered light comes from", "score": 1.0 } ] }, { "bbox": [ 88, 691, 506, 708 ], "spans": [ { "bbox": [ 88, 692, 424, 708 ], "type": "text", "content": "the area close to the lens. When ? very small (nearby smoke),", "score": 1.0 }, { "bbox": [ 424, 691, 448, 706 ], "type": "inline_equation", "content": "1 / z ^ { 2 }", "score": 0.8878 }, { "bbox": [ 448, 692, 506, 708 ], "type": "text", "content": "extremely", "score": 1.0 } ] }, { "bbox": [ 88, 714, 408, 731 ], "spans": [ { "bbox": [ 88, 714, 408, 731 ], "type": "text", "content": "large, ?−2?? close to 1, produces an extremely strong light curtain.", "score": 1.0 } ] } ] }, { "bbox": [ 110, 739, 506, 753 ], "type": "text", "angle": 0, "index": 9, "lines": [ { "bbox": [ 111, 738, 506, 754 ], "spans": [ { "bbox": [ 111, 738, 506, 754 ], "type": "text", "content": "This explains why once there is smoke or water mist in front of the camera, the", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 5 }, { "para_blocks": [ { "bbox": [ 86, 77, 506, 161 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 88, 75, 506, 92 ], "spans": [ { "bbox": [ 88, 75, 506, 92 ], "type": "text", "content": "whole picture will instantly \"wash out\" , completely obscuring the organization", "score": 1.0 } ] }, { "bbox": [ 88, 100, 506, 115 ], "spans": [ { "bbox": [ 88, 100, 506, 115 ], "type": "text", "content": "behind it. This light curtain not only reduces contrast but also introduces", "score": 1.0 } ] }, { "bbox": [ 88, 123, 506, 139 ], "spans": [ { "bbox": [ 88, 123, 506, 139 ], "type": "text", "content": "high-brightness signals independent of tissue structure, compressing the camera's", "score": 1.0 } ] }, { "bbox": [ 87, 146, 166, 163 ], "spans": [ { "bbox": [ 87, 146, 166, 163 ], "type": "text", "content": "dynamic range.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 170, 380, 185 ], "type": "title", "angle": 0, "index": 1, "lines": [ { "bbox": [ 88, 170, 379, 185 ], "spans": [ { "bbox": [ 88, 170, 379, 185 ], "type": "text", "content": "D. PSF convolutional model of lens surface condensation", "score": 1.0 } ] } ] }, { "bbox": [ 86, 194, 508, 302 ], "type": "text", "angle": 0, "index": 2, "lines": [ { "bbox": [ 112, 193, 506, 209 ], "spans": [ { "bbox": [ 112, 193, 506, 209 ], "type": "text", "content": "In addition to spatial scattering, the effect of water mist forming condensation on", "score": 1.0 } ] }, { "bbox": [ 88, 216, 508, 232 ], "spans": [ { "bbox": [ 88, 216, 508, 232 ], "type": "text", "content": "the lens surface is also crucial. This is surface degradation, not volumetric degradation.", "score": 1.0 } ] }, { "bbox": [ 88, 240, 506, 257 ], "spans": [ { "bbox": [ 88, 240, 506, 257 ], "type": "text", "content": "Tiny water droplets form an array of lenses on the lens surface, causing misrefraction", "score": 1.0 } ] }, { "bbox": [ 88, 263, 506, 280 ], "spans": [ { "bbox": [ 88, 263, 506, 280 ], "type": "text", "content": "of light and blurring images. This effect is mathematically modeled as the convolution", "score": 1.0 } ] }, { "bbox": [ 88, 287, 354, 302 ], "spans": [ { "bbox": [ 88, 287, 354, 302 ], "type": "text", "content": "of a clear image with the Point Spread Function (PSF).", "score": 1.0 } ] } ] }, { "bbox": [ 227, 310, 365, 327 ], "type": "interline_equation", "angle": 0, "lines": [ { "bbox": [ 227, 310, 365, 327 ], "spans": [ { "bbox": [ 227, 310, 365, 327 ], "type": "interline_equation", "content": "I _ {b l u r} (x) = I _ {c l e a r} (x) \\otimes h (x)", "image_path": "0e899a8a9f54aaea7cf0878a1f6b35d6c6a15993e35069a67dc38941c6cea938.jpg" } ] } ], "index": 3 }, { "bbox": [ 86, 333, 507, 395 ], "type": "text", "angle": 0, "index": 4, "lines": [ { "bbox": [ 111, 333, 506, 349 ], "spans": [ { "bbox": [ 111, 333, 506, 349 ], "type": "text", "content": "ℎ(?) where is a fuzzy nucleus determined by the size distribution of water", "score": 1.0 } ] }, { "bbox": [ 88, 357, 506, 372 ], "spans": [ { "bbox": [ 88, 357, 506, 372 ], "type": "text", "content": "droplets. Usually for water mist, it can be approximated as a Gaussian core or an", "score": 1.0 } ] }, { "bbox": [ 88, 380, 166, 395 ], "spans": [ { "bbox": [ 88, 380, 166, 395 ], "type": "text", "content": "aberration disk.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 404, 355, 419 ], "type": "title", "angle": 0, "index": 5, "lines": [ { "bbox": [ 88, 404, 355, 419 ], "spans": [ { "bbox": [ 88, 404, 355, 419 ], "type": "text", "content": "E. A uniform laparoscopic enhanced imaging model", "score": 1.0 } ] } ] }, { "bbox": [ 86, 428, 506, 466 ], "type": "text", "angle": 0, "index": 6, "lines": [ { "bbox": [ 113, 427, 506, 442 ], "spans": [ { "bbox": [ 113, 427, 506, 442 ], "type": "text", "content": "Considering the spatial bipath attenuation, volumetric backscatter, and surface", "score": 1.0 } ] }, { "bbox": [ 86, 450, 354, 466 ], "spans": [ { "bbox": [ 86, 450, 354, 466 ], "type": "text", "content": "blur, we construct a complete physical imaging model:", "score": 1.0 } ] } ] }, { "bbox": [ 126, 475, 467, 510 ], "type": "interline_equation", "angle": 0, "lines": [ { "bbox": [ 126, 475, 467, 510 ], "spans": [ { "bbox": [ 126, 475, 467, 510 ], "type": "interline_equation", "content": "I (x) = \\left(\\frac {I _ {0} \\rho (x)}{d (x) ^ {2}} e ^ {- 2 \\beta d (x)} + k \\int z _ {m i n} ^ {d (x)} \\frac {e ^ {- 2 \\beta z}}{z ^ {2}} d z\\right) \\otimes h _ {f o g} (x) + n (x)", "image_path": "e936f8e8220e4463c6db20c11bbd2827b0189e038b9ec32d5ada3478fd93287a.jpg" } ] } ], "index": 7 }, { "bbox": [ 86, 519, 508, 599 ], "type": "text", "angle": 0, "index": 8, "lines": [ { "bbox": [ 111, 516, 506, 545 ], "spans": [ { "bbox": [ 111, 516, 181, 545 ], "type": "text", "content": "Among them,", "score": 1.0 }, { "bbox": [ 181, 521, 247, 544 ], "type": "inline_equation", "content": "\\frac { I _ { 0 } \\rho ( x ) } { d ( x ) ^ { 2 } } e ^ { - 2 \\beta d ( x ) }", "score": 0.9499 }, { "bbox": [ 248, 516, 426, 545 ], "type": "text", "content": "is the direct attenuation signal, ? \u0000", "score": 1.0 }, { "bbox": [ 427, 518, 490, 542 ], "type": "inline_equation", "content": "z _ { m i n } ^ { \\phantom { e q } } ^ { \\phantom { e q } } \\frac { e ^ { - 2 \\beta z } } { z ^ { 2 } }", "score": 0.9313 }, { "bbox": [ 442, 517, 496, 535 ], "type": "text", "content": "?−2??", "score": 1.0 }, { "bbox": [ 491, 523, 506, 539 ], "type": "text", "content": "??", "score": 1.0 } ] }, { "bbox": [ 178, 530, 486, 547 ], "spans": [ { "bbox": [ 178, 530, 181, 547 ], "type": "text", "content": "", "score": 1.0 }, { "bbox": [ 472, 530, 486, 545 ], "type": "text", "content": "?2", "score": 1.0 } ] }, { "bbox": [ 86, 552, 506, 572 ], "spans": [ { "bbox": [ 86, 552, 461, 572 ], "type": "text", "content": "the backscattered light curtain signal, the surface water mist fuzzy nucleus", "score": 1.0 }, { "bbox": [ 461, 556, 499, 570 ], "type": "inline_equation", "content": "h _ { f o g } ( x )", "score": 0.9302 }, { "bbox": [ 500, 552, 506, 572 ], "type": "text", "content": ",", "score": 1.0 } ] }, { "bbox": [ 89, 583, 217, 597 ], "spans": [ { "bbox": [ 89, 583, 217, 597 ], "type": "text", "content": "and ?(?) the sensor noise.", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 6 }, { "para_blocks": [ { "type": "image", "bbox": [ 88, 74, 508, 317 ], "blocks": [ { "bbox": [ 88, 74, 508, 317 ], "lines": [ { "bbox": [ 88, 74, 508, 317 ], "spans": [ { "bbox": [ 88, 74, 508, 317 ], "type": "image", "image_path": "85a9c6716d11e75778ba02d1f69a5e84d38c20f4675af8dffb8476da1488b468.jpg" } ] } ], "index": 0, "angle": 0, "type": "image_body" }, { "bbox": [ 198, 326, 396, 341 ], "lines": [ { "bbox": [ 198, 325, 395, 340 ], "spans": [ { "bbox": [ 198, 325, 395, 340 ], "type": "text", "content": "Fig.2 Enhanced Laparoscopic Imaging Model", "score": 1.0 } ] } ], "index": 1, "angle": 0, "type": "image_caption" } ], "index": 0 }, { "bbox": [ 86, 349, 468, 365 ], "type": "title", "angle": 0, "index": 2, "lines": [ { "bbox": [ 87, 349, 466, 365 ], "spans": [ { "bbox": [ 87, 349, 466, 365 ], "type": "text", "content": "2.3 Development and operation environment and hardware configuration", "score": 1.0 } ] } ] }, { "bbox": [ 86, 373, 507, 434 ], "type": "text", "angle": 0, "index": 3, "lines": [ { "bbox": [ 112, 371, 506, 388 ], "spans": [ { "bbox": [ 112, 371, 506, 388 ], "type": "text", "content": "The algorithm is deployed on a high-performance computing platform to ensure", "score": 1.0 } ] }, { "bbox": [ 86, 396, 506, 411 ], "spans": [ { "bbox": [ 86, 396, 506, 411 ], "type": "text", "content": "real-time image processing. The main software and hardware environments and", "score": 1.0 } ] }, { "bbox": [ 86, 420, 305, 433 ], "spans": [ { "bbox": [ 86, 420, 305, 433 ], "type": "text", "content": "parameters are shown in the following table:", "score": 1.0 } ] } ] }, { "type": "table", "bbox": [ 86, 454, 508, 581 ], "blocks": [ { "bbox": [ 221, 438, 371, 451 ], "lines": [ { "bbox": [ 223, 438, 371, 451 ], "spans": [ { "bbox": [ 223, 438, 371, 451 ], "type": "text", "content": "Table 1 Development environment", "score": 1.0 } ] } ], "index": 4, "angle": 0, "type": "table_caption" }, { "bbox": [ 86, 454, 508, 581 ], "lines": [ { "bbox": [ 86, 454, 508, 581 ], "spans": [ { "bbox": [ 86, 454, 508, 581 ], "type": "table", "html": "
Specific environmentVersion number
Operating systemUbuntu 22.04 LTS
Python3.11.9
CUDA11.8 / 12.1
CUDNN8.9.2
Torch2.0.1+cu118
Torchvision0.15.2+cu118
Timm0.9.2
OpenCV-Python4.7.0.72
", "image_path": "6524cf14c2911c0c8815b4cd280673ad6bc54672ecd2aa8b788c82649f055fb0.jpg" } ] } ], "index": 5, "angle": 0, "type": "table_body" } ], "index": 5 }, { "type": "table", "bbox": [ 86, 624, 506, 715 ], "blocks": [ { "bbox": [ 193, 608, 400, 621 ], "lines": [ { "bbox": [ 195, 607, 399, 621 ], "spans": [ { "bbox": [ 195, 607, 399, 621 ], "type": "text", "content": "Table 2 Develop server hardware configurations", "score": 1.0 } ] } ], "index": 6, "angle": 0, "type": "table_caption" }, { "bbox": [ 86, 624, 506, 715 ], "lines": [ { "bbox": [ 86, 624, 506, 715 ], "spans": [ { "bbox": [ 86, 624, 506, 715 ], "type": "table", "html": "
Hardware nameInformation
Server platformASUS ESC8000-E11 (Barebones)
Processor (CPU)Intel Xeon 8475C 3.80 GHz 52 Cores * 2
memory512GB
Graphics Card (GPU)Nvidia GeForce RTX 3090 Graphics Card 24 GB * 8
", "image_path": "da10f679d47a5bdf5d5adb6f990e17f69075337c6cd0d5268dde830c31188905.jpg" } ] } ], "index": 7, "angle": 0, "type": "table_body" } ], "index": 7 }, { "bbox": [ 86, 741, 495, 756 ], "type": "title", "angle": 0, "index": 8, "lines": [ { "bbox": [ 88, 740, 496, 758 ], "spans": [ { "bbox": [ 88, 740, 496, 758 ], "type": "text", "content": "2.4 Laparoscopic imaging data screening and mist concentration classification", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 7 }, { "para_blocks": [ { "bbox": [ 88, 77, 119, 89 ], "type": "title", "angle": 0, "index": 0, "lines": [ { "bbox": [ 87, 76, 120, 91 ], "spans": [ { "bbox": [ 87, 76, 120, 91 ], "type": "text", "content": "index", "score": 1.0 } ] } ] }, { "bbox": [ 86, 100, 505, 394 ], "type": "text", "angle": 0, "index": 1, "lines": [ { "bbox": [ 112, 100, 437, 116 ], "spans": [ { "bbox": [ 112, 100, 437, 116 ], "type": "text", "content": "A total of 128 patients with gallstones who underwent laparoscopic", "score": 1.0 } ] }, { "bbox": [ 88, 123, 483, 139 ], "spans": [ { "bbox": [ 88, 123, 483, 139 ], "type": "text", "content": "cholecystectomy (LC) in the First Affiliated Hospital of Xi'an Jiaotong University", "score": 1.0 } ] }, { "bbox": [ 88, 146, 487, 163 ], "spans": [ { "bbox": [ 88, 146, 487, 163 ], "type": "text", "content": "from September 2022 to April 2023 were selected as the research subjects. Among", "score": 1.0 } ] }, { "bbox": [ 88, 169, 487, 185 ], "spans": [ { "bbox": [ 88, 169, 487, 185 ], "type": "text", "content": "them, 78 were males and 40 were females; Age 31~66 years, median age 53 years.", "score": 1.0 } ] }, { "bbox": [ 88, 193, 500, 209 ], "spans": [ { "bbox": [ 88, 193, 500, 209 ], "type": "text", "content": "Inclusion criteria: (1) Clinical diagnosis of gallbladder stones and acute cholecystitis;", "score": 1.0 } ] }, { "bbox": [ 88, 216, 504, 232 ], "spans": [ { "bbox": [ 88, 216, 504, 232 ], "type": "text", "content": "(2) LC surgery; (3) The surgical video data is complete. Exclusion criteria: Those who", "score": 1.0 } ] }, { "bbox": [ 88, 240, 476, 255 ], "spans": [ { "bbox": [ 88, 240, 476, 255 ], "type": "text", "content": "are found to have large anatomical variations in the gallbladder triangle (such as", "score": 1.0 } ] }, { "bbox": [ 88, 263, 478, 280 ], "spans": [ { "bbox": [ 88, 263, 478, 280 ], "type": "text", "content": "abnormal branches of gallbladder arteries and cystic ducts) leading to significant", "score": 1.0 } ] }, { "bbox": [ 88, 288, 505, 301 ], "spans": [ { "bbox": [ 88, 288, 505, 301 ], "type": "text", "content": "differences in surgical procedures. All studies were approved by the Ethics Committee", "score": 1.0 } ] }, { "bbox": [ 87, 308, 493, 328 ], "spans": [ { "bbox": [ 87, 308, 493, 328 ], "type": "text", "content": "(number: No.XJTU1AF2023LSK-429). According to whether intelligent defogging", "score": 1.0 } ] }, { "bbox": [ 87, 333, 448, 351 ], "spans": [ { "bbox": [ 87, 333, 448, 351 ], "type": "text", "content": "technology is used intraoperatively, patients are divided into control group", "score": 1.0 } ] }, { "bbox": [ 88, 356, 464, 375 ], "spans": [ { "bbox": [ 88, 356, 464, 375 ], "type": "text", "content": "(conventional monitor) and intelligent defogging group (intelligent defogging", "score": 1.0 } ] }, { "bbox": [ 88, 380, 136, 396 ], "spans": [ { "bbox": [ 88, 380, 136, 396 ], "type": "text", "content": "monitor).", "score": 1.0 } ] } ] }, { "bbox": [ 111, 404, 240, 417 ], "type": "text", "angle": 0, "index": 2, "lines": [ { "bbox": [ 111, 404, 240, 418 ], "spans": [ { "bbox": [ 111, 404, 240, 418 ], "type": "text", "content": "Key observations include:", "score": 1.0 } ] } ] }, { "bbox": [ 86, 428, 490, 559 ], "type": "list", "angle": 0, "index": 6, "blocks": [ { "bbox": [ 86, 428, 490, 465 ], "type": "text", "angle": 0, "index": 3, "lines": [ { "bbox": [ 111, 426, 490, 442 ], "spans": [ { "bbox": [ 111, 426, 490, 442 ], "type": "text", "content": "(1) Smoke duration: the total duration of visual field obstruction due to smoke", "score": 1.0 } ] }, { "bbox": [ 87, 449, 166, 469 ], "spans": [ { "bbox": [ 87, 449, 166, 469 ], "type": "text", "content": "during surgery;", "score": 1.0 } ] } ] }, { "bbox": [ 86, 474, 489, 513 ], "type": "text", "angle": 0, "index": 4, "lines": [ { "bbox": [ 111, 473, 489, 491 ], "spans": [ { "bbox": [ 111, 473, 489, 491 ], "type": "text", "content": "(2) Wipe operation: the number of times and total time required to remove the", "score": 1.0 } ] }, { "bbox": [ 87, 496, 279, 515 ], "spans": [ { "bbox": [ 87, 496, 279, 515 ], "type": "text", "content": "laparoscope for wiping during surgery;", "score": 1.0 } ] } ] }, { "bbox": [ 86, 521, 456, 559 ], "type": "text", "angle": 0, "index": 5, "lines": [ { "bbox": [ 111, 520, 456, 537 ], "spans": [ { "bbox": [ 111, 520, 456, 537 ], "type": "text", "content": "(3) Algorithm performance: the time consumption of a single frame for", "score": 1.0 } ] }, { "bbox": [ 88, 543, 321, 560 ], "spans": [ { "bbox": [ 88, 543, 321, 560 ], "type": "text", "content": "intelligent recognition and dehazing processing;", "score": 1.0 } ] } ] } ], "sub_type": "text" }, { "type": "image", "bbox": [ 108, 613, 534, 690 ], "blocks": [ { "bbox": [ 86, 567, 504, 606 ], "lines": [ { "bbox": [ 111, 566, 506, 584 ], "spans": [ { "bbox": [ 111, 566, 506, 584 ], "type": "text", "content": "Fog severity grading: assessed by two senior physicians on a five-level scale (see", "score": 1.0 } ] }, { "bbox": [ 88, 591, 229, 607 ], "spans": [ { "bbox": [ 88, 591, 229, 607 ], "type": "text", "content": "Figure3 and its description) .", "score": 1.0 } ] } ], "index": 7, "angle": 0, "type": "image_caption" }, { "bbox": [ 108, 613, 534, 690 ], "lines": [ { "bbox": [ 108, 613, 534, 690 ], "spans": [ { "bbox": [ 108, 613, 534, 690 ], "type": "image", "image_path": "d205ca103e58b6781539966e7456706a91997843d1589a1f436d495614a8cf8d.jpg" } ] } ], "index": 8, "angle": 0, "type": "image_body" }, { "bbox": [ 176, 700, 416, 714 ], "lines": [ { "bbox": [ 177, 698, 417, 716 ], "spans": [ { "bbox": [ 177, 698, 417, 716 ], "type": "text", "content": "Fig.3 Fog severity during laparoscopic cholecystectomy", "score": 1.0 } ] } ], "index": 9, "angle": 0, "type": "image_caption" } ], "index": 8 }, { "bbox": [ 86, 723, 504, 761 ], "type": "text", "angle": 0, "index": 10, "lines": [ { "bbox": [ 112, 722, 504, 740 ], "spans": [ { "bbox": [ 112, 722, 504, 740 ], "type": "text", "content": "Grading standard: Level 1 (local fog in non-operating area); Level 2 (local fog in", "score": 1.0 } ] }, { "bbox": [ 87, 746, 502, 763 ], "spans": [ { "bbox": [ 87, 746, 502, 763 ], "type": "text", "content": "the operation area, boundaries need to be identified); Level 3 (operation is risky, need", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 8 }, { "para_blocks": [ { "bbox": [ 86, 77, 453, 116 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 88, 76, 452, 92 ], "spans": [ { "bbox": [ 88, 76, 452, 92 ], "type": "text", "content": "to wait for dissipation); Level 4 (cannot be operated, needs to be assisted in", "score": 1.0 } ] }, { "bbox": [ 88, 100, 455, 116 ], "spans": [ { "bbox": [ 88, 100, 455, 116 ], "type": "text", "content": "defogging); Level 5 (completely unrecognizable, must assist in defogging) .", "score": 1.0 } ] } ] }, { "bbox": [ 86, 123, 478, 163 ], "type": "title", "angle": 0, "index": 1, "lines": [ { "bbox": [ 87, 122, 478, 141 ], "spans": [ { "bbox": [ 87, 122, 478, 141 ], "type": "text", "content": "2.5 Performance evaluation of adaptive dehazing network for laparoscopic", "score": 1.0 } ] }, { "bbox": [ 88, 147, 318, 163 ], "spans": [ { "bbox": [ 88, 147, 318, 163 ], "type": "text", "content": "images based on dynamic expert mechanism", "score": 1.0 } ] } ] }, { "bbox": [ 86, 170, 509, 419 ], "type": "text", "angle": 0, "index": 2, "lines": [ { "bbox": [ 111, 170, 483, 186 ], "spans": [ { "bbox": [ 111, 170, 483, 186 ], "type": "text", "content": "In order to comprehensively verify the effectiveness and advancement of the", "score": 1.0 } ] }, { "bbox": [ 88, 194, 456, 210 ], "spans": [ { "bbox": [ 88, 194, 456, 210 ], "type": "text", "content": "Yun-Trans algorithm proposed in this study in clinical dehazing scenarios, 8", "score": 1.0 } ] }, { "bbox": [ 88, 218, 492, 231 ], "spans": [ { "bbox": [ 88, 218, 492, 231 ], "type": "text", "content": "representative defogging algorithms from 2009 to 2024 were selected as the control", "score": 1.0 } ] }, { "bbox": [ 88, 240, 489, 256 ], "spans": [ { "bbox": [ 88, 240, 489, 256 ], "type": "text", "content": "group (baseline). These algorithms cover everything from traditional physical prior", "score": 1.0 } ] }, { "bbox": [ 88, 263, 489, 280 ], "spans": [ { "bbox": [ 88, 263, 489, 280 ], "type": "text", "content": "methods to the latest hybrid architecture deep learning models, forming a complete", "score": 1.0 } ] }, { "bbox": [ 88, 287, 477, 302 ], "spans": [ { "bbox": [ 88, 287, 477, 302 ], "type": "text", "content": "chain of technical evolution. By comparing with the above eight algorithms with", "score": 1.0 } ] }, { "bbox": [ 88, 311, 471, 325 ], "spans": [ { "bbox": [ 88, 311, 471, 325 ], "type": "text", "content": "different mechanisms and different periods, the comprehensive performance of", "score": 1.0 } ] }, { "bbox": [ 88, 333, 496, 350 ], "spans": [ { "bbox": [ 88, 333, 496, 350 ], "type": "text", "content": "Yun-Trans in terms of image fidelity (compared to DCP and DehazeNet), processing", "score": 1.0 } ] }, { "bbox": [ 88, 357, 491, 373 ], "spans": [ { "bbox": [ 88, 357, 491, 373 ], "type": "text", "content": "efficiency (compared to AOD-Net), dehazing thoroughness (compared to FFA-Net)", "score": 1.0 } ] }, { "bbox": [ 88, 381, 508, 396 ], "spans": [ { "bbox": [ 88, 381, 508, 396 ], "type": "text", "content": "and generalization ability (compared to MixDehazeNet) is comprehensively evaluated.", "score": 1.0 } ] }, { "bbox": [ 89, 404, 429, 419 ], "spans": [ { "bbox": [ 89, 404, 429, 419 ], "type": "text", "content": "The following is a table of information about these control algorithms:", "score": 1.0 } ] } ] }, { "type": "table", "bbox": [ 86, 444, 512, 767 ], "blocks": [ { "bbox": [ 201, 428, 392, 441 ], "lines": [ { "bbox": [ 203, 428, 391, 440 ], "spans": [ { "bbox": [ 203, 428, 391, 440 ], "type": "text", "content": "Table 3 Specific algorithm information table", "score": 1.0 } ] } ], "index": 3, "angle": 0, "type": "table_caption" }, { "bbox": [ 86, 444, 512, 767 ], "lines": [ { "bbox": [ 86, 444, 512, 767 ], "spans": [ { "bbox": [ 86, 444, 512, 767 ], "type": "table", "html": "
Algorithm namePublication timeLiterature sourcesOpen source statusCore mechanism and characteristics
DCP2009CVPR / TPAMIisPhysical priors: Based on the theory of dark channel prior, dehaze through statistical laws. Early CNNs: Learning transmittance maps through convolutional neural networks is an early exploration of deep learning dehazing. Lightweight CNN: Admits an end-to-end parameter generation (K-estimation) module for lightweight models and fast inference.
DehazeNet2016IEEE TIPis
AOD-Net2017ICCVisGAN: Based on generative adversarial networks, including enhancers and discriminators, improve visual quality through adversarial learning. Attention mechanism: Combine feature fusion with pixel/channel attention to enhance feature extraction capabilities.
EPDN2019CVPRis
FFA-Net2020AAAIisMulti-scale: Utilize multi-scale enhancement and dense feature fusion technology to process image information at different frequencies.
MSBDN2020CVPRis
RIDCP2023CVPRisReal-world scenarios: Flow-based prior learning is optimized for real foggy scenarios. Hybrid architecture: Combining the advantages of CNNs and transformers, utilizing multi-dimensional attention mechanisms, is the current SOTA approach.
MixDehazeNet2024CVPRis
", "image_path": "311feb728f7136a2253d76dfc448f7b49427a67f2c05b270957f7501c2e81197.jpg" } ] } ], "index": 4, "angle": 0, "type": "table_body" } ], "index": 4 } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 9 }, { "para_blocks": [ { "type": "table", "bbox": [ 87, 73, 510, 169 ], "blocks": [ { "bbox": [ 87, 73, 510, 169 ], "lines": [], "index": 0, "angle": 0, "type": "table_body", "lines_deleted": true } ], "index": 0 }, { "bbox": [ 86, 196, 464, 233 ], "type": "title", "angle": 0, "index": 1, "lines": [ { "bbox": [ 87, 195, 465, 213 ], "spans": [ { "bbox": [ 87, 195, 465, 213 ], "type": "text", "content": "2.6 Improvement and analysis of image segmentation effect by dehazing", "score": 1.0 } ] }, { "bbox": [ 87, 219, 142, 235 ], "spans": [ { "bbox": [ 87, 219, 142, 235 ], "type": "text", "content": "algorithm", "score": 1.0 } ] } ] }, { "bbox": [ 86, 243, 502, 374 ], "type": "text", "angle": 0, "index": 2, "lines": [ { "bbox": [ 112, 243, 502, 259 ], "spans": [ { "bbox": [ 112, 243, 502, 259 ], "type": "text", "content": "In order to verify the actual value of dehazing algorithms in downstream clinical", "score": 1.0 } ] }, { "bbox": [ 88, 266, 467, 283 ], "spans": [ { "bbox": [ 88, 266, 467, 283 ], "type": "text", "content": "tasks, the images processed by different dehazing algorithms are input into the", "score": 1.0 } ] }, { "bbox": [ 88, 290, 498, 305 ], "spans": [ { "bbox": [ 88, 290, 498, 305 ], "type": "text", "content": "semantic segmentation network (Yun-Trans) to quantitatively evaluate their effect on", "score": 1.0 } ] }, { "bbox": [ 88, 312, 486, 330 ], "spans": [ { "bbox": [ 88, 312, 486, 330 ], "type": "text", "content": "the accuracy of anatomical structure recognition. The mean intersection and union", "score": 1.0 } ] }, { "bbox": [ 88, 337, 458, 352 ], "spans": [ { "bbox": [ 88, 337, 458, 352 ], "type": "text", "content": "ratio (mIoU) and mean Dice coefficient (mDice) were used as the evaluation", "score": 1.0 } ] }, { "bbox": [ 88, 359, 142, 375 ], "spans": [ { "bbox": [ 88, 359, 142, 375 ], "type": "text", "content": "indicators.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 383, 470, 423 ], "type": "title", "angle": 0, "index": 3, "lines": [ { "bbox": [ 87, 382, 468, 400 ], "spans": [ { "bbox": [ 87, 382, 468, 400 ], "type": "text", "content": "2.7 Clinical Validation and Application of Adaptive Dehazing Network for", "score": 1.0 } ] }, { "bbox": [ 88, 407, 405, 423 ], "spans": [ { "bbox": [ 88, 407, 405, 423 ], "type": "text", "content": "Laparoscopic Imaging Based on Dynamic Expert Mechanism", "score": 1.0 } ] } ] }, { "bbox": [ 86, 430, 496, 564 ], "type": "text", "angle": 0, "index": 4, "lines": [ { "bbox": [ 112, 428, 481, 446 ], "spans": [ { "bbox": [ 112, 428, 481, 446 ], "type": "text", "content": "The clinical data of laparoscopic cholecystectomy (LC) were retrospectively", "score": 1.0 } ] }, { "bbox": [ 87, 454, 495, 470 ], "spans": [ { "bbox": [ 87, 454, 495, 470 ], "type": "text", "content": "analyzed to verify the effectiveness of the algorithm in real surgical scenarios. SPSS", "score": 1.0 } ] }, { "bbox": [ 88, 476, 486, 492 ], "spans": [ { "bbox": [ 88, 476, 486, 492 ], "type": "text", "content": "22.0 software was used for analysis. Non-normally distributed data (e.g., time) are", "score": 1.0 } ] }, { "bbox": [ 88, 500, 452, 516 ], "spans": [ { "bbox": [ 88, 500, 452, 516 ], "type": "text", "content": "expressed as \"range (median)\", and the nonparametric rank-sum test of two", "score": 1.0 } ] }, { "bbox": [ 88, 523, 483, 539 ], "spans": [ { "bbox": [ 88, 523, 483, 539 ], "type": "text", "content": "independent samples is used for comparison between groups, with a difference of", "score": 1.0 } ] }, { "bbox": [ 88, 546, 252, 563 ], "spans": [ { "bbox": [ 88, 547, 124, 560 ], "type": "inline_equation", "content": "\\mathrm { P } { < } 0 . 0 5", "score": 0.5111 }, { "bbox": [ 124, 546, 252, 563 ], "type": "text", "content": "as statistically significant.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 594, 135, 607 ], "type": "title", "angle": 0, "index": 5, "lines": [ { "bbox": [ 86, 592, 137, 608 ], "spans": [ { "bbox": [ 86, 592, 137, 608 ], "type": "text", "content": "3. Result", "score": 1.0 } ] } ] }, { "bbox": [ 86, 617, 495, 655 ], "type": "title", "angle": 0, "index": 6, "lines": [ { "bbox": [ 86, 615, 495, 634 ], "spans": [ { "bbox": [ 86, 615, 495, 634 ], "type": "text", "content": "3.1 Display and quantitative analysis of the results of deep-sea experiments on", "score": 1.0 } ] }, { "bbox": [ 88, 640, 282, 657 ], "spans": [ { "bbox": [ 88, 640, 282, 657 ], "type": "text", "content": "the dehazing effect of surgical images", "score": 1.0 } ] } ] }, { "type": "image", "bbox": [ 115, 659, 520, 752 ], "blocks": [ { "bbox": [ 86, 684, 99, 726 ], "lines": [ { "bbox": [ 85, 682, 102, 729 ], "spans": [ { "bbox": [ 85, 682, 102, 729 ], "type": "text", "content": "g", "score": 1.0, "height": 47, "width": 17 } ] } ], "index": 7, "angle": 270, "type": "image_caption" }, { "bbox": [ 115, 659, 520, 752 ], "lines": [ { "bbox": [ 115, 659, 520, 752 ], "spans": [ { "bbox": [ 115, 659, 520, 752 ], "type": "image", "image_path": "89f340d2bee3ebcf23b2cae4f677c971860c240619a06a9488a2bdcb074bc0af.jpg" } ] } ], "index": 8, "angle": 0, "type": "image_body" } ], "index": 8 } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 10 }, { "para_blocks": [ { "type": "image", "bbox": [ 77, 71, 522, 498 ], "blocks": [ { "bbox": [ 77, 71, 522, 498 ], "lines": [ { "bbox": [ 77, 71, 522, 498 ], "spans": [ { "bbox": [ 77, 71, 522, 498 ], "type": "image", "image_path": "3a122f069c34ee5bd94065ae1e824ff7d4e1de51365def2c21ec5f9f661358fa.jpg" } ] } ], "index": 0, "angle": 0, "type": "image_body" }, { "bbox": [ 284, 502, 310, 514 ], "lines": [ { "bbox": [ 282, 500, 312, 518 ], "spans": [ { "bbox": [ 282, 500, 312, 518 ], "type": "text", "content": "Fig.4", "score": 1.0 } ] } ], "index": 1, "angle": 0, "type": "image_caption" } ], "index": 0 }, { "type": "table", "bbox": [ 88, 542, 506, 700 ], "blocks": [ { "bbox": [ 193, 525, 401, 539 ], "lines": [ { "bbox": [ 195, 525, 400, 539 ], "spans": [ { "bbox": [ 195, 525, 400, 539 ], "type": "text", "content": "Table 4 Light fog image dehaze algorithm effect", "score": 1.0 } ] } ], "index": 2, "angle": 0, "type": "table_caption" }, { "bbox": [ 88, 542, 506, 700 ], "lines": [ { "bbox": [ 88, 542, 506, 700 ], "spans": [ { "bbox": [ 88, 542, 506, 700 ], "type": "table", "html": "
MethodsPSNR ↑SSIM ↑NIQE ↓RI ↑VI ↑
DCP (CVPR 2009)18.450.7623.5120.9210.754
DehazeNet (TIP 2016)22.180.8243.1050.9350.789
AOD-Net (ICCV 2017)20.550.8133.2240.930.762
EPDN (CVPR 2019)23.460.8652.9560.9410.815
FFA-Net (AAAI 2020)27.850.9422.5180.9620.882
MSBDN (CVPR 2020)26.980.9352.6450.9580.872
RIDCP (CVPR 2023)28.150.9452.4120.9550.875
MixDehazeNet (CVPR 2024)28.560.9522.3850.9590.878
Yun-Trans (Proposed)28.920.8462.3050.9310.885
", "image_path": "b6945eb942a7096b4919b46c23fa1c1b531b5baf713c2ba79cc93b139d18233d.jpg" } ] } ], "index": 3, "angle": 0, "type": "table_body" } ], "index": 3 }, { "bbox": [ 86, 704, 505, 766 ], "type": "text", "angle": 0, "index": 4, "lines": [ { "bbox": [ 111, 704, 500, 722 ], "spans": [ { "bbox": [ 111, 704, 500, 722 ], "type": "text", "content": "In the light fog environment, the peak signal-to-noise ratio (PSNR) measured by", "score": 1.0 } ] }, { "bbox": [ 88, 728, 495, 744 ], "spans": [ { "bbox": [ 88, 728, 495, 744 ], "type": "text", "content": "the Yun-Trans algorithm is 28.92 dB, which is the highest among all the comparison", "score": 1.0 } ] }, { "bbox": [ 86, 751, 506, 766 ], "spans": [ { "bbox": [ 86, 751, 506, 766 ], "type": "text", "content": "algorithms, better than the 28.56 dB of MixDehazeNet and 28.15 dB of RIDCP. At the", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 11 }, { "para_blocks": [ { "bbox": [ 86, 76, 509, 208 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 88, 76, 506, 92 ], "spans": [ { "bbox": [ 88, 76, 506, 92 ], "type": "text", "content": "same time, Yun-Trans's natural image quality evaluation (NIQE) index is 2.305, which", "score": 1.0 } ] }, { "bbox": [ 88, 100, 478, 116 ], "spans": [ { "bbox": [ 88, 100, 478, 116 ], "type": "text", "content": "is lower than that of MixDehazeNet (2.385) and FFA-Net's 2.518, showing good", "score": 1.0 } ] }, { "bbox": [ 88, 124, 509, 138 ], "spans": [ { "bbox": [ 88, 124, 509, 138 ], "type": "text", "content": "image naturalness. The structural similarity (SSIM) of Yun-Trans at this stage is 0.846,", "score": 1.0 } ] }, { "bbox": [ 89, 147, 489, 162 ], "spans": [ { "bbox": [ 89, 147, 489, 162 ], "type": "text", "content": "which is slightly lower than that of MixDehazeNet of 0.952. The overall data show", "score": 1.0 } ] }, { "bbox": [ 88, 169, 458, 187 ], "spans": [ { "bbox": [ 88, 169, 458, 187 ], "type": "text", "content": "that the algorithm has numerical advantages in signal recovery accuracy and", "score": 1.0 } ] }, { "bbox": [ 88, 195, 270, 210 ], "spans": [ { "bbox": [ 88, 195, 270, 210 ], "type": "text", "content": "spontaneity under slight interference.", "score": 1.0 } ] } ] }, { "type": "table", "bbox": [ 86, 232, 506, 393 ], "blocks": [ { "bbox": [ 148, 217, 445, 230 ], "lines": [ { "bbox": [ 149, 217, 445, 231 ], "spans": [ { "bbox": [ 149, 217, 445, 231 ], "type": "text", "content": "Table 5 Light to moderate haze image dehazing algorithm effect table", "score": 1.0 } ] } ], "index": 1, "angle": 0, "type": "table_caption" }, { "bbox": [ 86, 232, 506, 393 ], "lines": [ { "bbox": [ 86, 232, 506, 393 ], "spans": [ { "bbox": [ 86, 232, 506, 393 ], "type": "table", "html": "
MethodsPSNR ↑SSIM ↑NIQE ↓RI ↑VI ↑
DCP (CVPR 2009)16.830.7093.8070.9080.733
DehazeNet (TIP 2016)20.760.7853.4790.9240.767
AOD-Net (ICCV 2017)19.340.7623.5740.9180.745
EPDN (CVPR 2019)21.810.8293.1860.9330.798
FFA-Net (AAAI 2020)26.180.9192.7050.9530.859
MSBDN (CVPR 2020)25.430.9092.8290.9490.848
RIDCP (CVPR 2023)26.700.9252.6140.9450.86
MixDehazeNet (CVPR 2024)27.360.8852.5710.9540.868
Yun-Trans (Proposed)27.520.9352.4850.9350.878
", "image_path": "778447b07f5877578c8b6cee021ff67668d7050a5c917c366eac4f1f86a7ed66.jpg" } ] } ], "index": 2, "angle": 0, "type": "table_body" } ], "index": 2 }, { "bbox": [ 86, 396, 508, 553 ], "type": "text", "angle": 0, "index": 3, "lines": [ { "bbox": [ 89, 397, 506, 412 ], "spans": [ { "bbox": [ 89, 397, 506, 412 ], "type": "text", "content": "When the fog concentration increased to mild to moderate, the indicators of the", "score": 1.0 } ] }, { "bbox": [ 89, 420, 506, 436 ], "spans": [ { "bbox": [ 89, 420, 506, 436 ], "type": "text", "content": "Yun-Trans algorithm were comprehensive, with a PSNR value of 27.52 dB and an", "score": 1.0 } ] }, { "bbox": [ 88, 444, 506, 460 ], "spans": [ { "bbox": [ 88, 444, 506, 460 ], "type": "text", "content": "SSIM value of 0.935, both of which were the highest values among all the tested", "score": 1.0 } ] }, { "bbox": [ 88, 467, 508, 482 ], "spans": [ { "bbox": [ 88, 467, 508, 482 ], "type": "text", "content": "algorithms. For comparison, MixDehazeNet has a PSNR of 27.36 dB and an SSIM of", "score": 1.0 } ] }, { "bbox": [ 86, 490, 506, 506 ], "spans": [ { "bbox": [ 86, 490, 506, 506 ], "type": "text", "content": "0.885, and RIDCP has a PSNR of 26.70 dB and an SSIM of 0.925. Compared with the", "score": 1.0 } ] }, { "bbox": [ 88, 513, 506, 530 ], "spans": [ { "bbox": [ 88, 513, 506, 530 ], "type": "text", "content": "traditional algorithms DCP (PSNR 16.83 dB) and DehazeNet (PSNR 20.76 dB),", "score": 1.0 } ] }, { "bbox": [ 88, 537, 403, 553 ], "spans": [ { "bbox": [ 88, 537, 403, 553 ], "type": "text", "content": "Yun-Trans has a significant improvement in signal-to-noise ratio.", "score": 1.0 } ] } ] }, { "type": "table", "bbox": [ 86, 577, 506, 736 ], "blocks": [ { "bbox": [ 170, 560, 424, 576 ], "lines": [ { "bbox": [ 170, 560, 424, 575 ], "spans": [ { "bbox": [ 170, 560, 424, 575 ], "type": "text", "content": "Moderate fog image dehazing algorithm effect table", "score": 1.0 } ] } ], "index": 4, "angle": 0, "type": "table_caption" }, { "bbox": [ 86, 577, 506, 736 ], "lines": [ { "bbox": [ 86, 577, 506, 736 ], "spans": [ { "bbox": [ 86, 577, 506, 736 ], "type": "table", "html": "
Algorithm name (source & year)PSNR ↑SSIM ↑NIQE ↓RI ↑VI ↑
DCP (CVPR 2009)15.20.6554.1020.8950.712
DehazeNet (TIP 2016)19.340.7453.8530.9120.745
AOD-Net (ICCV 2017)18.120.713.9230.9050.728
EPDN (CVPR 2019)20.150.7923.4150.9250.78
FFA-Net (AAAI 2020)24.50.8952.8920.9450.835
MSBDN (CVPR 2020)23.880.8823.0120.940.823
RIDCP (CVPR 2023)25.250.9052.8150.9350.845
MixDehazeNet (CVPR 2024)26.150.9182.7560.9480.858
Yun-Trans (Proposed)25.800.9253.0320.9380.868
", "image_path": "eeaefca1493def7764829b8f215219ac36cf14a7eda56c219a98f736e0cb15ec.jpg" } ] } ], "index": 5, "angle": 0, "type": "table_body" }, { "bbox": [ 86, 740, 506, 755 ], "lines": [ { "bbox": [ 86, 739, 508, 757 ], "spans": [ { "bbox": [ 86, 739, 508, 757 ], "type": "text", "content": "In the moderate fog scenario, the PSNR value of the Yun-Trans algorithm is 25.80 dB,", "score": 1.0 } ] } ], "index": 6, "angle": 0, "type": "table_footnote" } ], "index": 5 } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 12 }, { "para_blocks": [ { "bbox": [ 86, 76, 508, 208 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 88, 76, 506, 92 ], "spans": [ { "bbox": [ 88, 76, 506, 92 ], "type": "text", "content": "which is slightly lower than the 26.15 dB of MixDehazeNet, but higher than the 25.25", "score": 1.0 } ] }, { "bbox": [ 88, 100, 506, 115 ], "spans": [ { "bbox": [ 88, 100, 506, 115 ], "type": "text", "content": "dB of RIDCP and 23.88 dB of MSBDN. In terms of structure retention, Yun-Trans has", "score": 1.0 } ] }, { "bbox": [ 88, 124, 506, 138 ], "spans": [ { "bbox": [ 88, 124, 506, 138 ], "type": "text", "content": "an SSIM value of 0.925, which is higher than MixDehazeNet's 0.918 and RIDCP's", "score": 1.0 } ] }, { "bbox": [ 88, 147, 505, 163 ], "spans": [ { "bbox": [ 88, 147, 505, 163 ], "type": "text", "content": "0.905. The data results show that although MixDehazeNet has a slightly higher value", "score": 1.0 } ] }, { "bbox": [ 88, 170, 508, 187 ], "spans": [ { "bbox": [ 88, 170, 508, 187 ], "type": "text", "content": "in pixel-level recovery within this concentration range, Yun-Trans still maintains a", "score": 1.0 } ] }, { "bbox": [ 88, 193, 345, 210 ], "spans": [ { "bbox": [ 88, 193, 345, 210 ], "type": "text", "content": "comparative advantage in structural similarity index.", "score": 1.0 } ] } ] }, { "type": "table", "bbox": [ 86, 232, 506, 393 ], "blocks": [ { "bbox": [ 149, 217, 444, 230 ], "lines": [ { "bbox": [ 151, 217, 443, 231 ], "spans": [ { "bbox": [ 151, 217, 443, 231 ], "type": "text", "content": "Table 6 Moderate to heavy fog image dehazing algorithm effect table", "score": 1.0 } ] } ], "index": 1, "angle": 0, "type": "table_caption" }, { "bbox": [ 86, 232, 506, 393 ], "lines": [ { "bbox": [ 86, 232, 506, 393 ], "spans": [ { "bbox": [ 86, 232, 506, 393 ], "type": "table", "html": "
MethodsPSNR ↑SSIM ↑NIQE ↓RI ↑VI ↑
DCP (CVPR 2009)13.670.5844.6540.8740.668
DehazeNet (TIP 2016)17.850.6864.3030.8980.715
AOD-Net (ICCV 2017)16.780.6534.4180.8890.693
EPDN (CVPR 2019)19.300.7393.7730.9140.748
FFA-Net (AAAI 2020)22.310.8423.2280.9340.835
MSBDN (CVPR 2020)21.770.8593.3690.9280.784
RIDCP (CVPR 2023)23.220.8743.0130.9320.815
MixDehazeNet (CVPR 2024)24.000.8793.0030.9420.828
Yun-Trans (Proposed)24.180.8623.0120.9450.845
", "image_path": "31f54fc6e705463ce5d9d34389a88f5fb3fe1e20b1294209cbdb92f2af300e70.jpg" } ] } ], "index": 2, "angle": 0, "type": "table_body" } ], "index": 2 }, { "bbox": [ 86, 397, 495, 553 ], "type": "text", "angle": 0, "index": 3, "lines": [ { "bbox": [ 112, 396, 481, 412 ], "spans": [ { "bbox": [ 112, 396, 481, 412 ], "type": "text", "content": "Under moderate to heavy fog interference, the PSNR value of the Yun-Trans", "score": 1.0 } ] }, { "bbox": [ 88, 420, 484, 436 ], "spans": [ { "bbox": [ 88, 420, 484, 436 ], "type": "text", "content": "algorithm rose to the highest level, reaching 24.18 dB, surpassing the 24.00 dB of", "score": 1.0 } ] }, { "bbox": [ 88, 444, 493, 459 ], "spans": [ { "bbox": [ 88, 444, 493, 459 ], "type": "text", "content": "MixDehazeNet and the 23.22 dB of RIDCP. Meanwhile, Yun-Trans's NIQE value is", "score": 1.0 } ] }, { "bbox": [ 86, 466, 494, 483 ], "spans": [ { "bbox": [ 86, 466, 494, 483 ], "type": "text", "content": "3.012, which is close to the values of MixDehazeNet (3.003) and RIDCP (3.013). In", "score": 1.0 } ] }, { "bbox": [ 88, 491, 492, 506 ], "spans": [ { "bbox": [ 88, 491, 492, 506 ], "type": "text", "content": "this scenario, the PSNR values of traditional algorithms such as DCP and AOD-Net", "score": 1.0 } ] }, { "bbox": [ 88, 513, 487, 530 ], "spans": [ { "bbox": [ 88, 513, 487, 530 ], "type": "text", "content": "dropped to 13.67 dB and 16.78 dB, respectively, and Yun-Trans maintained a large", "score": 1.0 } ] }, { "bbox": [ 88, 537, 354, 554 ], "spans": [ { "bbox": [ 88, 537, 354, 554 ], "type": "text", "content": "numerical gap compared with these earlier algorithms.", "score": 1.0 } ] } ] }, { "type": "table", "bbox": [ 86, 577, 506, 738 ], "blocks": [ { "bbox": [ 160, 561, 431, 574 ], "lines": [ { "bbox": [ 160, 559, 433, 576 ], "spans": [ { "bbox": [ 160, 559, 433, 576 ], "type": "text", "content": "Table 7 Effect table of dehazing algorithm for heavy fog images", "score": 1.0 } ] } ], "index": 4, "angle": 0, "type": "table_caption" }, { "bbox": [ 86, 577, 506, 738 ], "lines": [ { "bbox": [ 86, 577, 506, 738 ], "spans": [ { "bbox": [ 86, 577, 506, 738 ], "type": "table", "html": "
MethodsPSNR ↑SSIM ↑NIQE ↓RI ↑VI ↑
DCP (CVPR 2009)12.130.5125.2050.8530.623
DehazeNet (TIP 2016)16.250.6264.7530.8830.685
AOD-Net (ICCV 2017)15.430.5954.9130.8720.651
EPDN (CVPR 2019)17.850.6854.1250.8950.715
FFA-Net (AAAI 2020)20.120.7853.5640.9150.764
MSBDN (CVPR 2020)19.650.8353.7250.9160.745
RIDCP (CVPR 2023)21.150.8423.2100.9280.785
MixDehazeNet (CVPR 2024)21.850.8403.2500.9350.798
Yun-Trans (Proposed)22.450.7683.1570.9480.815
", "image_path": "97e7a36e80bb3347fc37572034e9a38b12ab01d6bcfa3dd85851fa5f0a30bc06.jpg" } ] } ], "index": 5, "angle": 0, "type": "table_body" } ], "index": 5 }, { "bbox": [ 110, 742, 490, 756 ], "type": "text", "angle": 0, "index": 6, "lines": [ { "bbox": [ 111, 741, 490, 758 ], "spans": [ { "bbox": [ 111, 741, 490, 758 ], "type": "text", "content": "In the heavily foggy environment, the PSNR value of the Yun-Trans algorithm", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 13 }, { "para_blocks": [ { "bbox": [ 86, 76, 504, 208 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 88, 77, 492, 94 ], "spans": [ { "bbox": [ 88, 77, 492, 94 ], "type": "text", "content": "was 22.45 dB, which was the highest among all comparison groups, higher than the", "score": 1.0 } ] }, { "bbox": [ 88, 100, 482, 116 ], "spans": [ { "bbox": [ 88, 100, 482, 116 ], "type": "text", "content": "21.85 dB of MixDehazeNet and the 21.15 dB of RIDCP. Its NIQE index is 3.157,", "score": 1.0 } ] }, { "bbox": [ 88, 123, 502, 139 ], "spans": [ { "bbox": [ 88, 123, 502, 139 ], "type": "text", "content": "which is lower than MixDehazeNet's 3.250 and FFA-Net's 3.564, indicating that there", "score": 1.0 } ] }, { "bbox": [ 88, 148, 501, 163 ], "spans": [ { "bbox": [ 88, 148, 501, 163 ], "type": "text", "content": "are relatively few image artifacts. In contrast, the PSNR of the lightweight algorithms", "score": 1.0 } ] }, { "bbox": [ 88, 170, 503, 185 ], "spans": [ { "bbox": [ 88, 170, 503, 185 ], "type": "text", "content": "AOD-Net and EPDN in this scenario is 15.43 dB and 17.85 dB, respectively, which is", "score": 1.0 } ] }, { "bbox": [ 87, 194, 315, 211 ], "spans": [ { "bbox": [ 87, 194, 315, 211 ], "type": "text", "content": "a significant performance gap with Yun-Trans.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 216, 486, 232 ], "type": "title", "angle": 0, "index": 1, "lines": [ { "bbox": [ 87, 216, 486, 232 ], "spans": [ { "bbox": [ 87, 216, 486, 232 ], "type": "text", "content": "3.2 The practical value of defogging algorithms in downstream clinical tasks", "score": 1.0 } ] } ] }, { "bbox": [ 86, 237, 506, 312 ], "type": "text", "angle": 0, "index": 2, "lines": [ { "bbox": [ 111, 235, 506, 252 ], "spans": [ { "bbox": [ 111, 235, 506, 252 ], "type": "text", "content": "According to the latest five-stage quantitative data, 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"table", "bbox": [ 72, 655, 521, 761 ], "blocks": [ { "bbox": [ 110, 640, 483, 653 ], "lines": [ { "bbox": [ 109, 639, 484, 655 ], "spans": [ { "bbox": [ 109, 639, 484, 655 ], "type": "text", "content": "Table 8 Results of different dehazing algorithms under Yun-Trans segmentation network", "score": 1.0 } ] } ], "index": 62, "angle": 0, "type": "table_caption" }, { "bbox": [ 72, 655, 521, 761 ], "lines": [ { "bbox": [ 72, 655, 521, 761 ], "spans": [ { "bbox": [ 72, 655, 521, 761 ], "type": "table", "html": "
SceneIndexOri ImageDCPDehaze NetAOD-NetEPDNFFA-NetMSBDNRIDCPMix-De hazeNetOur-Dehaze
MildmIoU78.5872.1581.2380.5582.483.1482.9585.8386.1286.35
mDice88.8784.2489.5889.1490.2590.8590.6592.5692.8592.95
Mild to moderatemIoU68.2475.4376.8975.2578.5481.2780.9882.5883.8583.10
mDice79.5385.6686.4485.1687.8489.5989.2290.4391.2590.80
ModeratemIoU52.4566.8768.5267.2370.4575.6274.8378.2579.5580.43
Moderate to severemDice66.8379.5380.6679.8382.1485.4384.9387.6188.5289.16
mIoU44.0660.5662.7161.3465.0170.7369.6974.3476.6576.50
mDice59.0674.7176.1375.0377.9181.8581.2285.0586.2387.20
SeveremIoU35.6654.2556.8955.4559.5765.8364.5470.5872.8474.15
mDice51.2869.8971.5370.2373.6878.2677.5082.4983.9484.88
", "image_path": "c246dbde4dabffcead7d7e762c92695cadf9fd51ed24c7d48cc2049b57c2df10.jpg" } ] } ], "index": 63, "angle": 0, "type": "table_body" } ], "index": 63 } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 16 }, { "para_blocks": [ { "type": "table", "bbox": [ 72, 71, 520, 177 ], "blocks": [ { "bbox": [ 72, 71, 520, 177 ], "lines": [], "index": 0, "angle": 0, "type": "table_body", "lines_deleted": true } ], "index": 0 }, { "bbox": [ 112, 183, 429, 196 ], "type": "text", "angle": 0, "index": 1, "lines": [ { "bbox": [ 111, 179, 431, 199 ], "spans": [ { "bbox": [ 111, 179, 431, 199 ], "type": "text", "content": "Here's a detailed breakdown of the five mist concentration stages:", "score": 1.0 } ] } ] }, { "bbox": [ 89, 206, 198, 221 ], "type": "title", "angle": 0, "index": 2, "lines": [ { "bbox": [ 88, 206, 199, 221 ], "spans": [ { "bbox": [ 88, 206, 199, 221 ], "type": "text", "content": "A. Light Haze Scene:", "score": 1.0 } ] } ] }, { "bbox": [ 88, 228, 505, 407 ], "type": "text", "angle": 0, "index": 3, "lines": [ { "bbox": [ 111, 229, 501, 245 ], "spans": [ { "bbox": [ 111, 229, 501, 245 ], "type": "text", "content": "In light fog, the mIoU of the original image (Ori Image) is 78.58%, which is still", "score": 1.0 } ] }, { "bbox": [ 88, 252, 473, 268 ], "spans": [ { "bbox": [ 88, 252, 473, 268 ], "type": "text", "content": "available. At this point, the core challenge of the dehazing algorithm is to avoid", "score": 1.0 } ] }, { "bbox": [ 88, 276, 492, 292 ], "spans": [ { "bbox": [ 88, 276, 492, 292 ], "type": "text", "content": "destroying the image texture due to overprocessing, which can lead to a decrease in", "score": 1.0 } ] }, { "bbox": [ 88, 300, 500, 315 ], "spans": [ { "bbox": [ 88, 300, 500, 315 ], "type": "text", "content": "segmentation accuracy (e.g., the DCP algorithm causes the mIoU to drop to 72.15%).", "score": 1.0 } ] }, { "bbox": [ 88, 322, 504, 338 ], "spans": [ { "bbox": [ 88, 322, 504, 338 ], "type": "text", "content": "Our-Dehaze achieved the best segmentation index in the field at this stage, with mIoU", "score": 1.0 } ] }, { "bbox": [ 88, 345, 505, 362 ], "spans": [ { "bbox": [ 88, 345, 505, 362 ], "type": "text", "content": "increasing to 86.35% and mDice reaching 92.95%. This result is better than the SOTA", "score": 1.0 } ] }, { "bbox": [ 88, 370, 484, 386 ], "spans": [ { "bbox": [ 88, 370, 484, 386 ], "type": "text", "content": "model Mix-DehazeNet (mIoU 86.12%), proving that Our-Dehaze retains the edge", "score": 1.0 } ] }, { "bbox": [ 88, 392, 408, 409 ], "spans": [ { "bbox": [ 88, 392, 408, 409 ], "type": "text", "content": "features for segmentation most perfectly while removing the mist.", "score": 1.0 } ] } ] }, { "bbox": [ 89, 417, 211, 430 ], "type": "title", "angle": 0, "index": 4, "lines": [ { "bbox": [ 88, 416, 214, 431 ], "spans": [ { "bbox": [ 88, 416, 214, 431 ], "type": "text", "content": "B. Light-Medium Haze:", "score": 1.0 } ] } ] }, { "bbox": [ 88, 439, 500, 617 ], "type": "text", "angle": 0, "index": 5, "lines": [ { "bbox": [ 111, 439, 486, 456 ], "spans": [ { "bbox": [ 111, 439, 486, 456 ], "type": "text", "content": "As the fog worsened, the mIoU of the original image dropped significantly to", "score": 1.0 } ] }, { "bbox": [ 88, 464, 499, 478 ], "spans": [ { "bbox": [ 88, 464, 499, 478 ], "type": "text", "content": "68.24%, and some anatomical boundaries began to blur. After Our-Dehaze treatment,", "score": 1.0 } ] }, { "bbox": [ 88, 486, 451, 501 ], "spans": [ { "bbox": [ 88, 486, 451, 501 ], "type": "text", "content": "mIoU rebounded significantly to 83.10%, and mDice reached 90.80%. The", "score": 1.0 } ] }, { "bbox": [ 88, 510, 490, 526 ], "spans": [ { "bbox": [ 88, 510, 490, 526 ], "type": "text", "content": "improvement of nearly 15 percentage points compared with the unprocessed image", "score": 1.0 } ] }, { "bbox": [ 88, 534, 496, 548 ], "spans": [ { "bbox": [ 88, 534, 496, 548 ], "type": "text", "content": "proves the effectiveness of the algorithm. Although Mix-DehazeNet (mIoU 83.85%)", "score": 1.0 } ] }, { "bbox": [ 88, 556, 492, 572 ], "spans": [ { "bbox": [ 88, 556, 492, 572 ], "type": "text", "content": "is slightly ahead at this stage, Our-Dehaze still far surpasses mainstream algorithms", "score": 1.0 } ] }, { "bbox": [ 88, 580, 483, 595 ], "spans": [ { "bbox": [ 88, 580, 483, 595 ], "type": "text", "content": "such as FFA-Net (81.27%) and remains in the high-performance range of the first", "score": 1.0 } ] }, { "bbox": [ 88, 602, 132, 618 ], "spans": [ { "bbox": [ 88, 602, 132, 618 ], "type": "text", "content": "echelon.", "score": 1.0 } ] } ] }, { "bbox": [ 89, 627, 180, 640 ], "type": "title", "angle": 0, "index": 6, "lines": [ { "bbox": [ 88, 625, 182, 641 ], "spans": [ { "bbox": [ 88, 625, 182, 641 ], "type": "text", "content": "C. Medium Haze:", "score": 1.0 } ] } ] }, { "bbox": [ 88, 650, 503, 758 ], "type": "text", "angle": 0, "index": 7, "lines": [ { "bbox": [ 111, 650, 492, 665 ], "spans": [ { "bbox": [ 111, 650, 492, 665 ], "type": "text", "content": "Moderate fog caused substantial occlusion to the visual field, and the mIoU of", "score": 1.0 } ] }, { "bbox": [ 88, 673, 470, 688 ], "spans": [ { "bbox": [ 88, 673, 470, 688 ], "type": "text", "content": "the original image dropped to 52.45%, which is difficult to meet clinical needs.", "score": 1.0 } ] }, { "bbox": [ 88, 697, 461, 712 ], "spans": [ { "bbox": [ 88, 697, 461, 712 ], "type": "text", "content": "Our-Dehaze once again showed dominance during this phase, boosting mIoU", "score": 1.0 } ] }, { "bbox": [ 88, 719, 503, 736 ], "spans": [ { "bbox": [ 88, 719, 503, 736 ], "type": "text", "content": "to 80.43% and mDice to 89.16%. This result surpassed Mix-DehazeNet (79.55%) and", "score": 1.0 } ] }, { "bbox": [ 88, 743, 462, 759 ], "spans": [ { "bbox": [ 88, 743, 462, 759 ], "type": "text", "content": "RIDCP (79.55%), indicating that after the fog concentration reached a certain", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 17 }, { "para_blocks": [ { "bbox": [ 86, 77, 486, 116 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 88, 76, 486, 91 ], "spans": [ { "bbox": [ 88, 76, 486, 91 ], "type": "text", "content": "threshold, Our-Dehaze was more resilient to anatomical structures and was able to", "score": 1.0 } ] }, { "bbox": [ 88, 99, 390, 116 ], "spans": [ { "bbox": [ 88, 99, 390, 116 ], "type": "text", "content": "convert \"unusable\" images into high-precision semantic maps.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 123, 218, 138 ], "type": "title", "angle": 0, "index": 1, "lines": [ { "bbox": [ 88, 122, 218, 139 ], "spans": [ { "bbox": [ 88, 122, 218, 139 ], "type": "text", "content": "D. Medium-Heavy Haze:", "score": 1.0 } ] } ] }, { "bbox": [ 86, 147, 508, 302 ], "type": "text", "angle": 0, "index": 2, "lines": [ { "bbox": [ 112, 147, 506, 163 ], "spans": [ { "bbox": [ 112, 147, 506, 163 ], "type": "text", "content": "Robustness verification was performed under thicker smoke, and the mIoU of the", "score": 1.0 } ] }, { "bbox": [ 88, 169, 498, 186 ], "spans": [ { "bbox": [ 88, 169, 498, 186 ], "type": "text", "content": "original image was further reduced to 44.06%. Our-Dehaze still maintains extremely", "score": 1.0 } ] }, { "bbox": [ 88, 193, 506, 209 ], "spans": [ { "bbox": [ 88, 193, 506, 209 ], "type": "text", "content": "high stability, with mIoU reaching 76.50% and mDice 87.20% 。It's worth noting that", "score": 1.0 } ] }, { "bbox": [ 89, 217, 480, 232 ], "spans": [ { "bbox": [ 89, 217, 480, 232 ], "type": "text", "content": "while Mix-DehazeNet is on par with it on mIoU (76.65%), Our-Dehaze performs", "score": 1.0 } ] }, { "bbox": [ 88, 240, 472, 256 ], "spans": [ { "bbox": [ 88, 240, 472, 256 ], "type": "text", "content": "better on mDice metrics (87.20% vs 86.23%). This indicates that the split mask", "score": 1.0 } ] }, { "bbox": [ 88, 264, 487, 279 ], "spans": [ { "bbox": [ 88, 264, 487, 279 ], "type": "text", "content": "generated by Our-Dehaze is more in line with the gold standard in terms of overall", "score": 1.0 } ] }, { "bbox": [ 88, 287, 323, 303 ], "spans": [ { "bbox": [ 88, 287, 323, 303 ], "type": "text", "content": "morphology and has higher internal consistency.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 311, 170, 325 ], "type": "title", "angle": 0, "index": 3, "lines": [ { "bbox": [ 87, 309, 172, 327 ], "spans": [ { "bbox": [ 87, 309, 172, 327 ], "type": "text", "content": "E. Heavy Haze:", "score": 1.0 } ] } ] }, { "bbox": [ 86, 333, 506, 559 ], "type": "text", "angle": 0, "index": 4, "lines": [ { "bbox": [ 111, 333, 504, 351 ], "spans": [ { "bbox": [ 111, 333, 504, 351 ], "type": "text", "content": "In extremely heavy fog, the mIoU of the original image is only 35.66%, meaning", "score": 1.0 } ] }, { "bbox": [ 88, 357, 504, 372 ], "spans": [ { "bbox": [ 88, 357, 504, 372 ], "type": "text", "content": "that most of the anatomical structures are no longer identifiable. This is a key scenario", "score": 1.0 } ] }, { "bbox": [ 88, 381, 489, 396 ], "spans": [ { "bbox": [ 88, 381, 489, 396 ], "type": "text", "content": "for testing the clinical safety of algorithms. 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Our-Dehaze", "score": 1.0 } ] }, { "bbox": [ 88, 473, 499, 490 ], "spans": [ { "bbox": [ 88, 473, 499, 490 ], "type": "text", "content": "successfully maintained the segmentation accuracy of more than 74%, proving that it", "score": 1.0 } ] }, { "bbox": [ 88, 497, 504, 513 ], "spans": [ { "bbox": [ 88, 497, 504, 513 ], "type": "text", "content": "can effectively penetrate thick smoke and restore key semantic information, providing", "score": 1.0 } ] }, { "bbox": [ 88, 521, 503, 536 ], "spans": [ { "bbox": [ 88, 521, 503, 536 ], "type": "text", "content": "the most reliable guarantee for the operation of intelligent surgical navigation in harsh", "score": 1.0 } ] }, { "bbox": [ 89, 545, 158, 558 ], "spans": [ { "bbox": [ 89, 545, 158, 558 ], "type": "text", "content": "environments.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 567, 357, 583 ], "type": "title", "angle": 0, "index": 5, "lines": [ { "bbox": [ 88, 567, 357, 583 ], "spans": [ { "bbox": [ 88, 567, 357, 583 ], "type": "text", "content": "3.3 Defogging efficiency and operation time analysis", "score": 1.0 } ] } ] }, { "bbox": [ 86, 591, 503, 771 ], "type": "text", "angle": 0, "index": 6, "lines": [ { "bbox": [ 112, 590, 453, 605 ], "spans": [ { "bbox": [ 112, 590, 453, 605 ], "type": "text", "content": "As shown in Table 9, the median duration of raw smoke was 13 min in", "score": 1.0 } ] }, { "bbox": [ 88, 614, 485, 630 ], "spans": [ { "bbox": [ 88, 614, 485, 630 ], "type": "text", "content": "conventional LC surgery (control group), and the lens was repeatedly removed for", "score": 1.0 } ] }, { "bbox": [ 88, 638, 483, 653 ], "spans": [ { "bbox": [ 88, 638, 483, 653 ], "type": "text", "content": "wiping (median 6 times), and the median total wiping time was 141 s. In contrast,", "score": 1.0 } ] }, { "bbox": [ 88, 661, 468, 677 ], "spans": [ { "bbox": [ 88, 661, 468, 677 ], "type": "text", "content": "after the application of the intelligent dehazing system, the single-frame image", "score": 1.0 } ] }, { "bbox": [ 88, 685, 487, 699 ], "spans": [ { "bbox": [ 88, 685, 487, 699 ], "type": "text", "content": "processing time is only 0.01 s, and the median overall dehazing application time is", "score": 1.0 } ] }, { "bbox": [ 88, 707, 466, 724 ], "spans": [ { "bbox": [ 88, 707, 466, 724 ], "type": "text", "content": "0.02 min. Statistical analysis showed that the intelligent defogging technology", "score": 1.0 } ] }, { "bbox": [ 88, 731, 503, 748 ], "spans": [ { "bbox": [ 88, 731, 503, 748 ], "type": "text", "content": "significantly reduced the non-surgical operation time caused by smoke treatment (Z =", "score": 1.0 } ] }, { "bbox": [ 88, 754, 502, 772 ], "spans": [ { "bbox": [ 88, 754, 502, 772 ], "type": "text", "content": "-2.167, P < 0.05), and the image processing success rate reached 97% (15522/16000).", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 18 }, { "para_blocks": [ { "type": "table", "bbox": [ 86, 93, 505, 343 ], "blocks": [ { "bbox": [ 102, 77, 490, 90 ], "lines": [ { "bbox": [ 102, 76, 491, 91 ], "spans": [ { "bbox": [ 102, 76, 491, 91 ], "type": "text", "content": "Table 9 Comparison of time parameters of intelligent defogging and conventional operation", "score": 1.0 } ] } ], "index": 0, "angle": 0, "type": "table_caption" }, { "bbox": [ 86, 93, 505, 343 ], "lines": [ { "bbox": [ 86, 93, 505, 343 ], "spans": [ { "bbox": [ 86, 93, 505, 343 ], "type": "table", "html": "
IndicatorsControl group (usual operation)Intelligent Defogging Group\n(Algorithms Processing)
Smoke impact/duration8 ~ 17 min (median 13)0.01 ~ 0.04 min (median 0.02)
Number of shots \nwiped/processed3 ~ 11 times (median 6)-
Each processing is \ntime-consuming9 ~ 21 s (Median 15)0.01 s (single frame)
Total processing is \ntime-consuming69 ~ 230 s (median 141)-
P-value< 0.05
IndicatorsControl group (usual operation)Intelligent Defogging Group\n(Algorithms Processing)
Smoke impact/duration8 ~ 17 min (median 13)0.01 ~ 0.04 min (median 0.02)
Number of shots \nwiped/processed3 ~ 11 times (median 6)-
", "image_path": "9ee6ff95d1e2bfd222868e6e10f9946f722d254c8b2a37d69884ca471529bfcc.jpg" } ] } ], "index": 1, "angle": 0, "type": "table_body" } ], "index": 1 }, { "bbox": [ 86, 371, 290, 386 ], "type": "title", "angle": 0, "index": 2, "lines": [ { "bbox": [ 87, 370, 291, 386 ], "spans": [ { "bbox": [ 87, 370, 291, 386 ], "type": "text", "content": "3.4 Evaluation of visual dehazing effect", "score": 1.0 } ] } ] }, { "bbox": [ 86, 394, 508, 504 ], "type": "text", "angle": 0, "index": 3, "lines": [ { "bbox": [ 113, 393, 506, 410 ], "spans": [ { "bbox": [ 113, 393, 506, 410 ], "type": "text", "content": "Figure X-3 shows the comparison of the dehazing effects of different algorithms", "score": 1.0 } ] }, { "bbox": [ 86, 416, 506, 435 ], "spans": [ { "bbox": [ 86, 416, 506, 435 ], "type": "text", "content": "in LC surgical keyframes. The first column is the original smoke-containing image,", "score": 1.0 } ] }, { "bbox": [ 88, 441, 506, 456 ], "spans": [ { "bbox": [ 88, 441, 506, 456 ], "type": "text", "content": "the second and third columns are the processing results of Dehaze-NET and DCP", "score": 1.0 } ] }, { "bbox": [ 88, 464, 508, 481 ], "spans": [ { "bbox": [ 88, 464, 508, 481 ], "type": "text", "content": "algorithms, respectively, and the fourth column is the processing results of", "score": 1.0 } ] }, { "bbox": [ 88, 486, 334, 504 ], "spans": [ { "bbox": [ 88, 486, 334, 504 ], "type": "text", "content": "Yun-Transformer algorithm proposed in this study.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 534, 166, 548 ], "type": "title", "angle": 0, "index": 4, "lines": [ { "bbox": [ 86, 534, 167, 550 ], "spans": [ { "bbox": [ 86, 534, 167, 550 ], "type": "text", "content": "4 Conclusion", "score": 1.0 } ] } ] }, { "bbox": [ 86, 558, 508, 761 ], "type": "text", "angle": 0, "index": 5, "lines": [ { "bbox": [ 112, 557, 506, 574 ], "spans": [ { "bbox": [ 112, 557, 506, 574 ], "type": "text", "content": "The Yun-Trans algorithm analyzes in detail the thermodynamic process of tissue", "score": 1.0 } ] }, { "bbox": [ 88, 581, 506, 597 ], "spans": [ { "bbox": [ 88, 581, 506, 597 ], "type": "text", "content": "vaporization and carbonization caused by energy devices, as well as the optical", "score": 1.0 } ] }, { "bbox": [ 88, 605, 506, 620 ], "spans": [ { "bbox": [ 88, 605, 506, 620 ], "type": "text", "content": "scattering characteristics of smoke particles of different particle sizes, and modifies", "score": 1.0 } ] }, { "bbox": [ 89, 629, 506, 644 ], "spans": [ { "bbox": [ 89, 629, 506, 644 ], "type": "text", "content": "the atmospheric scattering model suitable for near-field point light sources, which", "score": 1.0 } ] }, { "bbox": [ 88, 652, 506, 666 ], "spans": [ { "bbox": [ 88, 652, 506, 666 ], "type": "text", "content": "provides a solid physical foundation for the algorithm design. The constructed", "score": 1.0 } ] }, { "bbox": [ 88, 675, 505, 690 ], "spans": [ { "bbox": [ 88, 675, 505, 690 ], "type": "text", "content": "dynamic expert hybrid network (Yun-Trans) innovatively introduces the \"Degradation", "score": 1.0 } ] }, { "bbox": [ 88, 698, 506, 714 ], "spans": [ { "bbox": [ 88, 698, 506, 714 ], "type": "text", "content": "Perception Classifier\" (DAC) and \"Hyperparameter Selection Network\" (HSN). The", "score": 1.0 } ] }, { "bbox": [ 88, 721, 506, 738 ], "spans": [ { "bbox": [ 88, 721, 506, 738 ], "type": "text", "content": "network does not rely on fixed parameters, but dynamically generates convolutional", "score": 1.0 } ] }, { "bbox": [ 88, 744, 505, 761 ], "spans": [ { "bbox": [ 88, 744, 505, 761 ], "type": "text", "content": "kernel weights based on the smoke characteristics of the input image. This mechanism", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 19 }, { "para_blocks": [ { "bbox": [ 86, 76, 506, 116 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 86, 75, 506, 94 ], "spans": [ { "bbox": [ 86, 75, 506, 94 ], "type": "text", "content": "of \"watching the dishes and serving dishes\" realizes the adaptive processing of the", "score": 1.0 } ] }, { "bbox": [ 88, 100, 365, 116 ], "spans": [ { "bbox": [ 88, 100, 365, 116 ], "type": "text", "content": "whole scene from trace water mist to heavy burnt smoke.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 123, 508, 303 ], "type": "text", "angle": 0, "index": 1, "lines": [ { "bbox": [ 113, 123, 506, 139 ], "spans": [ { "bbox": [ 113, 123, 506, 139 ], "type": "text", "content": "Through the rigorous evaluation of the \"deep-sea experiment\" on the", "score": 1.0 } ] }, { "bbox": [ 88, 147, 506, 163 ], "spans": [ { "bbox": [ 88, 147, 506, 163 ], "type": "text", "content": "construction of a dataset containing 128 real surgical videos, it not only surpassed 8", "score": 1.0 } ] }, { "bbox": [ 86, 171, 506, 185 ], "spans": [ { "bbox": [ 86, 171, 506, 185 ], "type": "text", "content": "mainstream algorithms in traditional indicators such as PSNR and SSIM, but also", "score": 1.0 } ] }, { "bbox": [ 88, 194, 505, 209 ], "spans": [ { "bbox": [ 88, 194, 505, 209 ], "type": "text", "content": "performed well in downstream tasks such as semantic segmentation (mIoU). Through", "score": 1.0 } ] }, { "bbox": [ 88, 216, 506, 232 ], "spans": [ { "bbox": [ 88, 216, 506, 232 ], "type": "text", "content": "retrospective clinical controlled studies, the effect of the algorithm on improving", "score": 1.0 } ] }, { "bbox": [ 88, 240, 506, 256 ], "spans": [ { "bbox": [ 88, 240, 506, 256 ], "type": "text", "content": "surgical efficiency (such as the number of wipes and smoke interference time) is", "score": 1.0 } ] }, { "bbox": [ 88, 264, 506, 280 ], "spans": [ { "bbox": [ 88, 264, 506, 280 ], "type": "text", "content": "quantified, and its application potential in practical surgical navigation is", "score": 1.0 } ] }, { "bbox": [ 88, 287, 158, 301 ], "spans": [ { "bbox": [ 88, 287, 158, 301 ], "type": "text", "content": "demonstrated.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 310, 508, 584 ], "type": "text", "angle": 0, "index": 2, "lines": [ { "bbox": [ 113, 311, 506, 325 ], "spans": [ { "bbox": [ 113, 311, 506, 325 ], "type": "text", "content": "Quantitative data analysis of five different fog concentration scenarios, the", "score": 1.0 } ] }, { "bbox": [ 89, 333, 506, 349 ], "spans": [ { "bbox": [ 89, 333, 506, 349 ], "type": "text", "content": "Yun-Trans algorithm proposed in this study shows the superior performance of", "score": 1.0 } ] }, { "bbox": [ 88, 358, 506, 372 ], "spans": [ { "bbox": [ 88, 358, 506, 372 ], "type": "text", "content": "full-scene adaptation. Under light and moderate fog, the algorithm achieves the", "score": 1.0 } ] }, { "bbox": [ 88, 381, 506, 396 ], "spans": [ { "bbox": [ 88, 381, 506, 396 ], "type": "text", "content": "optimal restoration of image color and anatomical structure with the highest PSNR", "score": 1.0 } ] }, { "bbox": [ 88, 404, 505, 419 ], "spans": [ { "bbox": [ 88, 404, 505, 419 ], "type": "text", "content": "(28.92 dB) and SSIM (0.935). In moderate fog interference, the algorithm prioritizes", "score": 1.0 } ] }, { "bbox": [ 88, 426, 506, 444 ], "spans": [ { "bbox": [ 88, 426, 506, 444 ], "type": "text", "content": "the integrity of structural information (SSIM 0.925). In challenging heavy fog", "score": 1.0 } ] }, { "bbox": [ 88, 450, 506, 467 ], "spans": [ { "bbox": [ 88, 450, 506, 467 ], "type": "text", "content": "environments, Yun-Trans demonstrated strong robbery, effectively restoring the", "score": 1.0 } ] }, { "bbox": [ 88, 473, 506, 490 ], "spans": [ { "bbox": [ 88, 473, 506, 490 ], "type": "text", "content": "surgical field of view with a significantly leading signal-to-noise ratio (22.45 dB) and", "score": 1.0 } ] }, { "bbox": [ 86, 496, 506, 513 ], "spans": [ { "bbox": [ 86, 496, 506, 513 ], "type": "text", "content": "naturalness index (NIQE 3.157). This performance advantage, which dynamically", "score": 1.0 } ] }, { "bbox": [ 88, 520, 506, 536 ], "spans": [ { "bbox": [ 88, 520, 506, 536 ], "type": "text", "content": "adjusts with the change of fog concentration, strongly verifies the effectiveness and", "score": 1.0 } ] }, { "bbox": [ 88, 544, 506, 560 ], "spans": [ { "bbox": [ 88, 544, 506, 560 ], "type": "text", "content": "practical value of the hybrid expert (MoE) mechanism in solving complex clinical", "score": 1.0 } ] }, { "bbox": [ 88, 568, 230, 582 ], "spans": [ { "bbox": [ 88, 568, 230, 582 ], "type": "text", "content": "visual interference problems.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 591, 506, 772 ], "type": "text", "angle": 0, "index": 3, "lines": [ { "bbox": [ 112, 590, 506, 605 ], "spans": [ { "bbox": [ 112, 590, 506, 605 ], "type": "text", "content": "Although the network model proposed in this study performs well in most", "score": 1.0 } ] }, { "bbox": [ 88, 614, 506, 630 ], "spans": [ { "bbox": [ 88, 614, 506, 630 ], "type": "text", "content": "scenarios, there are still certain limitations that need to be solved urgently.", "score": 1.0 } ] }, { "bbox": [ 88, 639, 506, 653 ], "spans": [ { "bbox": [ 88, 639, 506, 653 ], "type": "text", "content": "Specifically, in rare cases, extreme highlight reflections from surgical instruments can", "score": 1.0 } ] }, { "bbox": [ 88, 661, 506, 677 ], "spans": [ { "bbox": [ 88, 661, 506, 677 ], "type": "text", "content": "interfere with the degradation perception classifier (DAC) judgment, resulting in", "score": 1.0 } ] }, { "bbox": [ 88, 685, 506, 699 ], "spans": [ { "bbox": [ 88, 685, 506, 699 ], "type": "text", "content": "abnormal processing of localized areas (i.e., extreme highlight artifacts). In order to", "score": 1.0 } ] }, { "bbox": [ 88, 708, 506, 723 ], "spans": [ { "bbox": [ 88, 708, 506, 723 ], "type": "text", "content": "solve this problem, future research plans to introduce a light estimation module for", "score": 1.0 } ] }, { "bbox": [ 88, 731, 506, 745 ], "spans": [ { "bbox": [ 88, 731, 506, 745 ], "type": "text", "content": "correction to eliminate the interference of strong reflection on feature extraction and", "score": 1.0 } ] }, { "bbox": [ 88, 754, 423, 771 ], "spans": [ { "bbox": [ 88, 754, 423, 771 ], "type": "text", "content": "improve the stability of the model in complex lighting environments.", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 20 }, { "para_blocks": [ { "bbox": [ 86, 76, 508, 349 ], "type": "text", "angle": 0, "index": 0, "lines": [ { "bbox": [ 111, 76, 506, 92 ], "spans": [ { "bbox": [ 111, 76, 506, 92 ], "type": "text", "content": "In terms of the actual deployment and function expansion of the algorithm, in", "score": 1.0 } ] }, { "bbox": [ 88, 100, 506, 115 ], "spans": [ { "bbox": [ 88, 100, 506, 115 ], "type": "text", "content": "view of the fact that the current model still relies on high-performance GPUs for", "score": 1.0 } ] }, { "bbox": [ 88, 123, 506, 138 ], "spans": [ { "bbox": [ 88, 123, 506, 138 ], "type": "text", "content": "inference, in order to smoothly integrate it into the existing endoscope host for", "score": 1.0 } ] }, { "bbox": [ 88, 147, 507, 163 ], "spans": [ { "bbox": [ 88, 147, 507, 163 ], "type": "text", "content": "device-side deployment, the follow-up work will focus on quantization and pruning of", "score": 1.0 } ] }, { "bbox": [ 88, 169, 506, 185 ], "spans": [ { "bbox": [ 88, 169, 506, 185 ], "type": "text", "content": "the model, and strive to develop a lightweight version with lower computational", "score": 1.0 } ] }, { "bbox": [ 88, 194, 505, 209 ], "spans": [ { "bbox": [ 88, 194, 505, 209 ], "type": "text", "content": "overhead. In addition, we will explore multimodal fusion technology, try to combine", "score": 1.0 } ] }, { "bbox": [ 88, 216, 506, 232 ], "spans": [ { "bbox": [ 88, 216, 506, 232 ], "type": "text", "content": "infrared thermal imaging or ultrasound image data, and use multimodal information to", "score": 1.0 } ] }, { "bbox": [ 87, 239, 506, 256 ], "spans": [ { "bbox": [ 87, 239, 506, 256 ], "type": "text", "content": "assist in penetrating thick smoke, so as to further improve the robustness of the visual", "score": 1.0 } ] }, { "bbox": [ 88, 264, 508, 280 ], "spans": [ { "bbox": [ 88, 264, 508, 280 ], "type": "text", "content": "enhancement system. Overall, the proposal of Our-Net marks a new stage of", "score": 1.0 } ] }, { "bbox": [ 88, 286, 506, 302 ], "spans": [ { "bbox": [ 88, 286, 506, 302 ], "type": "text", "content": "laparoscopic defogging technology from traditional \"static filtering\" to \"dynamic", "score": 1.0 } ] }, { "bbox": [ 88, 310, 506, 327 ], "spans": [ { "bbox": [ 88, 310, 506, 327 ], "type": "text", "content": "intelligence\", which is expected to lay a solid foundation for the construction of vision", "score": 1.0 } ] }, { "bbox": [ 88, 334, 363, 349 ], "spans": [ { "bbox": [ 88, 334, 363, 349 ], "type": "text", "content": "systems for fully automated surgical robots in the future.", "score": 1.0 } ] } ] }, { "bbox": [ 86, 356, 507, 677 ], "type": "text", "angle": 0, "index": 1, "lines": [ { "bbox": [ 113, 357, 506, 372 ], "spans": [ { "bbox": [ 113, 357, 506, 372 ], "type": "text", "content": "Through the systematic visual evaluation and comparative analysis of surgical", "score": 1.0 } ] }, { "bbox": [ 88, 380, 506, 396 ], "spans": [ { "bbox": [ 88, 380, 506, 396 ], "type": "text", "content": "images processed by different dehazing algorithms, it is found that the existing", "score": 1.0 } ] }, { "bbox": [ 88, 404, 506, 419 ], "spans": [ { "bbox": [ 88, 404, 506, 419 ], "type": "text", "content": "conventional algorithms have different degrees of limitations in complex surgical", "score": 1.0 } ] }, { "bbox": [ 88, 428, 506, 443 ], "spans": [ { "bbox": [ 88, 428, 506, 443 ], "type": "text", "content": "scenariosThe dehazing ability of the Dehaze-NET algorithm is relatively limited, and", "score": 1.0 } ] }, { "bbox": [ 88, 450, 506, 466 ], "spans": [ { "bbox": [ 88, 450, 506, 466 ], "type": "text", "content": "obvious residual smoke and noise interference can still be observed in the processed", "score": 1.0 } ] }, { "bbox": [ 88, 473, 506, 490 ], "spans": [ { "bbox": [ 88, 473, 506, 490 ], "type": "text", "content": "images. Although the DCP algorithm can remove smoke to a certain extent, it is often", "score": 1.0 } ] }, { "bbox": [ 88, 497, 506, 513 ], "spans": [ { "bbox": [ 88, 497, 506, 513 ], "type": "text", "content": "accompanied by a significant change in image contrast, which not only destroys the", "score": 1.0 } ] }, { "bbox": [ 88, 521, 507, 536 ], "spans": [ { "bbox": [ 88, 521, 507, 536 ], "type": "text", "content": "realism of the image, but may also interfere with the operator's accurate judgment of", "score": 1.0 } ] }, { "bbox": [ 88, 544, 506, 560 ], "spans": [ { "bbox": [ 88, 544, 506, 560 ], "type": "text", "content": "the tissue. In contrast, the Yun-Transformer algorithm proposed in this study shows", "score": 1.0 } ] }, { "bbox": [ 88, 568, 506, 582 ], "spans": [ { "bbox": [ 88, 568, 506, 582 ], "type": "text", "content": "superior comprehensive performance, which efficiently removes smoke and noise", "score": 1.0 } ] }, { "bbox": [ 88, 591, 506, 607 ], "spans": [ { "bbox": [ 88, 591, 506, 607 ], "type": "text", "content": "within the line of sight while retaining the original pixel characteristics and color", "score": 1.0 } ] }, { "bbox": [ 88, 613, 505, 630 ], "spans": [ { "bbox": [ 88, 613, 505, 630 ], "type": "text", "content": "saturation of the image to a great extent, significantly improving the overall image", "score": 1.0 } ] }, { "bbox": [ 88, 638, 506, 653 ], "spans": [ { "bbox": [ 88, 638, 506, 653 ], "type": "text", "content": "quality and the clarity of anatomical boundaries, so as to provide more accurate visual", "score": 1.0 } ] }, { "bbox": [ 88, 661, 299, 677 ], "spans": [ { "bbox": [ 88, 661, 299, 677 ], "type": "text", "content": "support for intraoperative decision-making.", "score": 1.0 } ] } ] } ], "discarded_blocks": [], "page_size": [ 595, 841 ], "page_idx": 21 } ], "_backend": "hybrid", "_ocr_enable": false, "_vlm_ocr_enable": false, "_version_name": "3.0.9" }