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RefineDNet/README.md
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RefineDNet/README.md
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# RefineDNet for dehazing
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RefineDNet is a two-stage dehazing framework which can be weakly supervised using real-world unpaired images.
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That is, the training set never requires paired hazy and haze-free images coming from the same scene.
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In the first stage, it adopts DCP to restore visibility of the input hazy image.
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In the second stage, it improves the realness of preliminary results from the first stage via CNNs.
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RefineDNet is outlined in the following figure, and more details can be found in the [paper](https://doi.org/10.1109/TIP.2021.3060873) (or [this link](https://sse.tongji.edu.cn/linzhang/files/RefineDNet_TIP.pdf)) titled as _RefineDNet: A Weakly Supervised Refinement Framework for Single Image Dehazing._ (Early Access in Trans. Image Process.)
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# Our Environment
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- Ubuntu 16.06
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- Python (>= 3.5)
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- PyTorch (>= 1.1.0) with CUDA 9.0
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- torchvision (>=0.3.0)
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- numpy (>= 1.17.0)
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# Testing
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## Download the pretrained models.
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1. Get the model on [Google drive](https://drive.google.com/file/d/1NIm-o01AOdjGn3kvsVA57TEn6jYNKGr4/view?usp=sharing) or [BaiduYun Disk](https://pan.baidu.com/s/1pqy-Ka9b9xVaeumdNSZAWQ) (Key: bswu). It's trained on RESIDE-unpaired.
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2. Create a folder named `checkpoints`, and unzip `refined_DCP_outdoor.zip` in `./checkpoints`.
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Now, your directory tree should look like
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```
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<RefineDNet_root>
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├── checkpoints
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│ ├── refined_DCP_outdoor
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│ │ ├── 60_net_D.pth
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│ │ ├── 60_net_Refiner_J.pth
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│ │ ├── 60_net_Refiner_T.pth
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│ │ └── test_opt.txt
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│ ...
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...
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```
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## Quick test on real-world images
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1. Download the pretrained model on RESIDE-unpaired (see above).
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2. Run the following command from <RefineDNet_root>.
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```
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python quick_test.py --dataroot ./datasets/quick_test --dataset_mode single --name refined_DCP_outdoor --model refined_DCP --phase test --preprocess none --save_image --method_name refined_DCP_outdoor_ep_60 --epoch 60
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```
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The results will be saved in the folder `<RefineDNet_root>/datatsets/quick_test/refined_DCP_outdoor_ep_60`.
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## Test on BeDDE
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1. Download the pretrained model on BeDDE.
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2. Run the following command from `<RefineDNet_root>`.
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```
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python test_BeDDE.py --dataroot <BeDDE_root> --dataset_mode simple_bedde --bedde_list ./datasets/BeDDE/bedde_list.txt --name refined_DCP_outdoor --model refined_DCP --phase test --preprocess none --save_image --method_name refined_DCP_outdoor_ep_60 --epoch 60
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```
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The results will be saved in the folder `<BeDDE_root>/<city_name>/refined_DCP_outdoor_ep_60`.
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# Training
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## Train RefineDNet on RESIDE-unpaired
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1. Download RESIDE-unpaired on [Google drive](https://drive.google.com/file/d/1SjQwESy8nwVO7pC3JRW7vXvJ6Qqk6Et4/view?usp=sharing) or [BaiduYun Disk](https://pan.baidu.com/s/1pqy-Ka9b9xVaeumdNSZAWQ) (Key: bswu). Unzip `RESIDE-unpaired.zip` in the folder <RefineDNet_root>/datasets.
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Your directory tree should look like
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```
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<RefineDNet_root>
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├── datasets
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│ ├── BeDDE
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│ ├── RESIDE-unpaired
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│ │ ├── trainA
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│ │ └── trainB
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│ ...
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...
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```
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2. Open visdom by `python -m visdom.server`
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3. Run the following command from `<RefineDNet_root>`.
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```
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python train.py --dataroot ./datasets/RESIDE-unpaired --dataset_mode unpaired --model refined_DCP --name refined_DCP_outdoor --niter 30 --niter_decay 60 --lr_decay_iters 10 --preprocess scale_min_and_crop --load_size 300 --crop_size 256 --num_threads 8 --save_epoch_freq 3
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```
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## Train RefineDNet on ITS (from RESIDE-standard)
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1. Download ITS [here](https://sites.google.com/view/reside-dehaze-datasets/reside-standard?authuser=0). Unzip hazy.zip and clear.zip into `<RefineDNet_root>/datasets/ITS`.
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2. Rename the hazy image folder as `trainA` and the clear image folder as `trainB`.
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Then, your directory tree should look like
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```
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<RefineDNet_root>
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├── datasets
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│ ├── BeDDE
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│ ├── ITS
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│ │ ├── trainA
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│ │ └── trainB
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│ ...
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...
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```
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3. Open visdom by `python -m visdom.server`
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4. Run the following command from `<RefineDNet_root>`.
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```
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python train.py --dataroot ./datasets/ITS --dataset_mode unpaired --model refined_DCP --name refined_DCP_indoor --niter 30 --niter_decay 60 --lr_decay_iters 5 --preprocess scale_width_and_crop --load_size 372 --crop_size 256 --num_threads 8 --save_epoch_freq 1
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```
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# Results
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Some dehazing samples from BeDDE and the Internet produced by various methods.
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# Useful links
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1. [RESIDE dataset](https://sites.google.com/view/reside-dehaze-datasets/reside-standard?authuser=0)
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2. [BeDDE dataset](https://github.com/xiaofeng94/BeDDE-for-defogging)
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3. This code is based on [CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)
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