Official implementation.
by Xu Qin, Zhilin Wang et al. Peking University and Beijing University of Aeronautics & Astronautics.
To be determined.
- python3
- PyTorch>=1.0
- NVIDIA GPU+CUDA
- numpy
- matplotlib
- tensorboardX(optional)
Dataset website:RESIDE ; Paper arXiv version:[RESIDE: A Benchmark for Single Image Dehazing]
FILE STRUCTURE
FFA-Net
|-- README.md
|-- net
|-- data
|-- RESIDE
|-- ITS
|-- hazy
|-- *.png
|-- clear
|-- *.png
|-- OTS
|-- hazy
|-- *.jpg
|-- clear
|-- *.jpg
|-- SOTS
|-- indoor
|-- hazy
|-- *.png
|-- clear
|-- *.png
|-- outdoor
|-- hazy
|-- *.jpg
|-- clear
|-- *.png
| Methods | Indoor(PSNR/SSIM) | Outdoor(PSNR/SSIM) |
|---|---|---|
| DCP | 16.62/0.8179 | 19.13/0.8148 |
| AOD-Net | 19.06/0.8504 | 20.29/0.8765 |
| DehazeNet | 21.14/0.8472 | 22.46/0.8514 |
| GFN | 22.30/0.8800 | 21.55/0.8444 |
| GCANet | 30.23/0.9800 | -/- |
| Ours | 36.39/0.9886 | 33.57/0.9840 |
Remove annotation from main.py if you want to use tensorboard or view intermediate predictions
If you have more computing resources, expanding bs, crop_size, gps, blocks will lead to better results
train network on ITS dataset
python main.py --net='ffa' --crop --crop_size=240 --blocks=19 --gps=3 --bs=2 --lr=0.0001 --trainset='its_train' --testset='its_test' --steps=500000 --eval_step=5000train network on OTS dataset
python main.py --net='ffa' --crop --crop_size=240 --blocks=19 --gps=3 --bs=2 --lr=0.0001 --trainset='ots_train' --testset='ots_test' --steps=1000000 --eval_step=5000Trained_models are available at baidudrive: https://pan.baidu.com/s/1-pgSXN6-NXLzmTp21L_qIg with code: 4gat
or google drive: https://drive.google.com/drive/folders/19_lSUPrpLDZl9AyewhHBsHidZEpTMIV5?usp=sharing
Put models in the net/trained_models/folder.
Put your images in net/test_imgs/
python test.py --task='its or ots' --test_imgs='test_imgs'
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