README.md

Revitalizing Convolutional Network for Image Restoration

The official pytorch implementation of the paper Revitalizing Convolutional Network for Image Restoration

Yuning Cui, Wenqi Ren, Xiaochun Cao, Alois Knoll

News

All resulting images and pre-trained models are available in the provided links.

11/26/2024 Code for real haze and haze4k are released.

07/22/2024 We release the code for dehazing (ITS/OTS), desnowing, deraining, and motion deblurring.

Pretrained models

gdrive, 百度网盘

Installation

The project is built with PyTorch 3.8, PyTorch 1.8.1. CUDA 10.2, cuDNN 7.6.5 For installing, follow these instructions:

conda install pytorch=1.8.1 torchvision=0.9.1 -c pytorch
pip install tensorboard einops scikit-image pytorch_msssim opencv-python

Please use the pillow package downloaded by Conda rather than pip.

Install warmup scheduler:

cd pytorch-gradual-warmup-lr/
python setup.py install
cd ..

Training and Evaluation

Please refer to respective directories.

Results

Visualization Results: gdrive, 百度网盘

Model	Parameters	FLOPs
ConvIR-S (small)	5.53M	42.1G
ConvIR-B (base)	8.63M	71.22G
ConvIR-L (large)	14.83M	129.34G

Task	Dataset	PSNR	SSIM
Image Dehazing	SOTS-Indoor	41.53/42.72	0.996/0.997
	SOTS-Outdoor	37.95/39.42	0.994/0.996
	Haze4K	33.36/34.15/34.50	0.99/0.99/0.99
	Dense-Haze	17.45/16.86	0.648/0.621
	NH-HAZE	20.65/20.66	0.807/0.802
	O-HAZE	25.25/25.36	0.784/0.780
	I-HAZE	21.95/22.44	0.888/0.887
	SateHaze-1k-Thin/Moderate/Thick	25.11/26.79/22.65	0.978/0.978/0.950
	NHR	28.85/29.49	0.981/0.983
	GTA5	31.68/31.83	0.917/0.921
Image Desnowing	CSD	38.43/39.10	0.99/0.99
	SRRS	32.25/32.39	0.98/0.98
	Snow100K	33.79/33.92	0.95/0.96
Image Deraining	Test100	31.40	0.919
	Test2800	33.73	0.937
Defocus Deblurring	DPDD	26.06/26.16/26.36	0.810/0.814/0.820
Motion Deblurring	GoPro	33.28	0.963
	RSBlur	34.06	0.868

Citation

@article{cui2024revitalizing,
  title={Revitalizing Convolutional Network for Image Restoration},
  author={Cui, Yuning and Ren, Wenqi and Cao, Xiaochun and Knoll, Alois},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2024},
  publisher={IEEE}
}

@inproceedings{cui2023irnext,
  title={IRNeXt: Rethinking Convolutional Network Design for Image Restoration},
  author={Cui, Yuning and Ren, Wenqi and Yang, Sining and Cao, Xiaochun and Knoll, Alois},
  booktitle={International Conference on Machine Learning},
  pages={6545--6564},
  year={2023},
  organization={PMLR}
}

Contact

Should you have any problem, please contact Yuning Cui.