Attention Based CycleDehaze
Attention-based Single Image Dehazing Using Improved CycleGAN, IJCN IEEE WCCI 2022. Official Pytorch based implementation.
Model Architecture
Dependencies
- Python 3
- PyTorch >= 1.0
- NVIDIA GPU+CUDA
Dataset
Dataset used : RESIDE
File Structure
``` project │ README.md │ dataset.py │ main.py │ metrics.py │ option.py │ utility.py └───inputs | └───outputs | └───models | | dehaze.py | | dicriminator.py | | generator.py | └───DCNv2_latest | └───data │ └───haze │ | | *.png │ | │ └───clear │ | | *.png │ | │ └───SOTS │ └───indoor │ | └───haze │ | | | *.png │ | | │ | └───clear │ | | *.png │ | │ └───indoor │ └───haze │ | | *.png │ | │ └───clear │ | *.png | └───trained_models ```Metrics update
| Methods | Indoor(PSNR/SSIM) | Outdoor(PSNR/SSIM) |
|---|---|---|
| Paired Models | - | - |
| AOD-NET | 19.06/0.8504 | 20.29/0.8765 |
| DehazeNet | 21.14/0.8472 | 22.46/0.8514 |
| FFA-Net | 36.39/0.9886 | 33.57/0.9840 |
| Unpaired Models | - | - |
| DCP | 16.62/0.8179 | 19.13/0.8148 |
| Improved CycleGAN (with ssim loss) | 20.05/0.8307 | 21.14/0.8919 |
| Dehaze-GLCGAN | 23.03/0.9165 | 26.51/0.9354 |
| Ours | 31.67/0.9612 | 36.17/0.9745 |
Usage
Train
Unzip DCNv2_latest.zip inside models and build the files.
Train the model in ITS dataset.
python main.pyTest
Put your images in input.
python main.py --evalthe dehazed image will be saved at output
Samples
Acknowledgements
The code for DCN module implementation in PyTorch has been taken from DCNv2_latest.