The official pytorch implementation of the paper Simple Baselines for Image Restoration
Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods. In this paper, we propose a simple baseline that exceeds the SOTA methods and is computationally efficient. To further simplify the baseline, we reveal that the nonlinear activation functions, e.g. Sigmoid, ReLU, GELU, Softmax, etc. are not necessary: they could be replaced by multiplication or removed. Thus, we derive a Nonlinear Activation Free Network, namely NAFNet, from the baseline. SOTA results are achieved on various challenging benchmarks, e.g. 33.69 dB PSNR on GoPro (for image deblurring), exceeding the previous SOTA 0.38 dB with only 8.4% of its computational costs; 40.30 dB PSNR on SIDD (for image denoising), exceeding the previous SOTA 0.28 dB with less than half of its computational costs.
| Denoise | Deblur | StereoSR(NAFSSR) |
NAFNet based Stereo Image Super-Resolution solution (NAFSSR) won the 1st place on the NTIRE 2022 Stereo Image Super-resolution Challenge!
This implementation based on BasicSR which is a open source toolbox for image/video restoration tasks and HINet
python 3.9.5
pytorch 1.11.0
cuda 11.3git clone https://github.com/megvii-research/NAFNet
cd NAFNet
pip install -r requirements.txt
python setup.py develop --no_cuda_ext
-
Single Image Inference Demo:
- Image Denoise:
python basicsr/demo.py -opt options/test/SIDD/NAFNet-width64.yml --input_path ./demo/noisy.png --output_path ./demo/denoise_img.png- Image Deblur:
python basicsr/demo.py -opt options/test/GoPro/NAFNet-width64.yml --input_path ./demo/blurry.png --output_path ./demo/deblur_img.png--input_path: the path of the degraded image--output_path: the path to save the predicted image- pretrained models should be downloaded.
| name | Dataset | PSNR | SSIM | pretrained models | configs |
|---|---|---|---|---|---|
| NAFNet-GoPro-width32 | GoPro | 32.8705 | 0.9606 | gdrive | 百度网盘 | train | test |
| NAFNet-GoPro-width64 | GoPro | 33.7103 | 0.9668 | gdrive | 百度网盘 | train | test |
| NAFNet-SIDD-width32 | SIDD | 39.9672 | 0.9599 | gdrive | 百度网盘 | train | test |
| NAFNet-SIDD-width64 | SIDD | 40.3045 | 0.9614 | gdrive | 百度网盘 | train | test |
| NAFNet-REDS-width64 | REDS | 29.0903 | 0.8671 | gdrive | 百度网盘 | train | test |
| NAFSSR-L_4x | Flickr1024 | 24.17 | 0.7589 | gdrive | 百度网盘 | train | test |
| NAFSSR-L_2x | Flickr1024 | 29.68 | 0.9221 | gdrive | 百度网盘 | train | test |
| Task | Dataset | Instructions | Visualization Results |
|---|---|---|---|
| Image Deblurring | GoPro | link | gdrive | 百度网盘 |
| Image Denoising | SIDD | link | gdrive | 百度网盘 |
| Image Deblurring with JPEG artifacts | REDS | link | gdrive | 百度网盘 |
| Stereo Image Super-Resolution | Flickr1024+Middlebury | link | gdrive | 百度网盘 |
If NAFNet helps your research or work, please consider citing NAFNet.
@article{chen2022simple,
title={Simple Baselines for Image Restoration},
author={Chen, Liangyu and Chu, Xiaojie and Zhang, Xiangyu and Sun, Jian},
journal={arXiv preprint arXiv:2204.04676},
year={2022}
}
If you have any questions, please contact chenliangyu@megvii.com or chuxiaojie@megvii.com