This is the official PyTorch codes for the paper:
Curricular Contrastive Regularization for Physics-aware Single Image Dehazing
Yu Zheng, Jiahui Zhan, Shengfeng He, Junyu Dong, Yong Du* （ * indicates corresponding author)
IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Apr 20, 2023: We release training code.
We test the code on PyTorch 1.10.1 + CUDA 11.4 .
- Create a new conda environment
conda create -n c2pnet python=3.7.11
conda activate c2pnet
- Install dependencies
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt
You can download the pretrained models and datasets on Google Drive(pretrained models and testset).
The final file path will be arranged as (please check it carefully)：
|-trained_models
|- ITS.pkl
|- OTS.pkl
└─ ... (model name)
|-data
|-SOTS
|- indoor
|- hazy
|- 1400_1.png
|- 1401_1.png
└─ ... (image name)
|- clear
|- 1400.png
|- 1401.png
└─ ... (image name)
|- outdoor
|- hazy
|- 0001_0.8_0.2.jpg
|- 0002_0.8_0.08.jpg
└─ ... (image name)
|- clear
|- 0001.png
|- 0002.png
└─ ... (image name)
Since our training dataset contains numerous negative samples, which has led to an oversized dataset, we have only uploaded the ITS training dataset on BaiduNetdisk. However, users can create additional datasets using the create_lmdb.py, where they can define the number of negative samples and the types of existing dehazers used.
The final file path will be arranged as (please check it carefully)：
data
|-ITS
|- ITS.lmdb
|- ...(dataset name)
Test C2PNet on SOTS-indoor dataset
python dehaze.py -d indoor
Test C2PNet on SOTS-outdoor dataset
python dehaze.py -d outdoor
See python dehaze.py -h for the list of optional arguments
Train network on ITS dataset
python main.py --crop --crop_size=240 --blocks=19 --gps=3 --bs=2 --lr=0.0001 --trainset='its_train' --testset='its_test' --steps=1000000 --eval_step=5000 --clcrloss --clip
Train network on OTS dataset
python main.py --crop --crop_size=240 --blocks=19 --gps=3 --bs=2 --lr=0.0001 --trainset='ots_train' --testset='ots_test' --steps=1500000 --eval_step=5000 --clcrloss --clip
If you find our work useful for your research, please cite us:
@inproceedings{zheng2023curricular,
title={Curricular Contrastive Regularization for Physics-aware Single Image Dehazing},
author={Zheng, Yu and Zhan, Jiahui and He, Shengfeng and Dong, Junyu and Du, Yong},
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2023}
}
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