https://github.com/megvii-research/NAFNet
The state-of-the-art image restoration model without nonlinear activation functions.
https://github.com/megvii-research/NAFNet
deblur denoise eccv2022 image-deblurring image-denoising image-restoration low-level-vision pytorch stereo-super-resolution
Last synced: 3 months ago
JSON representation
The state-of-the-art image restoration model without nonlinear activation functions.
- Host: GitHub
- URL: https://github.com/megvii-research/NAFNet
- Owner: megvii-research
- License: other
- Created: 2022-04-10T11:59:05.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-03T13:26:24.000Z (over 1 year ago)
- Last Synced: 2024-08-08T23:21:03.618Z (about 1 year ago)
- Topics: deblur, denoise, eccv2022, image-deblurring, image-denoising, image-restoration, low-level-vision, pytorch, stereo-super-resolution
- Language: Python
- Homepage:
- Size: 15.8 MB
- Stars: 2,118
- Watchers: 21
- Forks: 261
- Open Issues: 102
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - megvii-research/NAFNet - research开发，旨在提供高效且高质量的图像复原方案。NAFNet的核心思想是利用简单的线性函数来构建网络，从而降低计算复杂度并提高运行效率。这种设计使得NAFNet在处理各种图像恢复任务时，能够达到甚至超越传统模型的性能。该项目提供了详细的代码实现、预训练模型和使用指南，方便研究人员和开发者进行实验和应用。NAFNet的成功证明了线性模型在图像处理领域的潜力，为未来的研究方向提供了新的思路。它在图像去噪、超分辨率等领域表现出色，为相关应用带来了显著的提升。该项目易于上手，并提供了丰富的文档支持，方便用户快速部署和使用。NAFNet的简洁性和高效性使其成为图像恢复领域的一个重要里程碑。 (图像恢复 / 资源传输下载)
README
[](https://paperswithcode.com/sota/image-deblurring-on-gopro?p=simple-baselines-for-image-restoration)
[](https://paperswithcode.com/sota/image-denoising-on-sidd?p=simple-baselines-for-image-restoration)
[](https://paperswithcode.com/sota/stereo-image-super-resolution-on-flickr1024-1?p=nafssr-stereo-image-super-resolution-using)
[](https://paperswithcode.com/sota/stereo-image-super-resolution-on-flickr1024-2?p=nafssr-stereo-image-super-resolution-using)
[](https://paperswithcode.com/sota/stereo-image-super-resolution-on-kitti2012-2x-1?p=nafssr-stereo-image-super-resolution-using)
[](https://paperswithcode.com/sota/stereo-image-super-resolution-on-kitti2012-4x?p=nafssr-stereo-image-super-resolution-using)
[](https://paperswithcode.com/sota/stereo-image-super-resolution-on-kitti2015-2x?p=nafssr-stereo-image-super-resolution-using)
[](https://paperswithcode.com/sota/stereo-image-super-resolution-on-kitti2015-4x?p=nafssr-stereo-image-super-resolution-using)
[](https://paperswithcode.com/sota/stereo-image-super-resolution-on-middlebury-1?p=nafssr-stereo-image-super-resolution-using)
[](https://paperswithcode.com/sota/stereo-image-super-resolution-on-middlebury?p=nafssr-stereo-image-super-resolution-using)
## NAFNet: Nonlinear Activation Free Network for Image Restoration
The official pytorch implementation of the paper **[Simple Baselines for Image Restoration (ECCV2022)](https://arxiv.org/abs/2204.04676)**
#### Liangyu Chen\*, Xiaojie Chu\*, Xiangyu Zhang, Jian Sun
>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](https://github.com/megvii-research/NAFNet/blob/main/docs/StereoSR.md)) |
### News
**2022.08.02** The Baseline, including the pretrained models and train/test configs, are available now.
**2022.07.03** Related work, [Improving Image Restoration by Revisiting Global Information Aggregation](https://arxiv.org/abs/2112.04491) (TLC, a.k.a TLSC in our paper) is accepted by **ECCV2022** :tada: . Code is available at https://github.com/megvii-research/TLC.
**2022.07.03** Our [paper](https://arxiv.org/abs/2204.04676) is accepted by **ECCV2022** :tada:
**2022.06.19** [NAFSSR](https://arxiv.org/abs/2204.08714) (as a challenge winner) is selected for an ORAL presentation at CVPR 2022, NTIRE workshop :tada: [Presentation video](https://drive.google.com/file/d/16w33zrb3UI0ZIhvvdTvGB2MP01j0zJve/view), [slides](https://data.vision.ee.ethz.ch/cvl/ntire22/slides/Chu_NAFSSR_slides.pdf) and [poster](https://data.vision.ee.ethz.ch/cvl/ntire22/posters/Chu_NAFSSR_poster.pdf) are available now.
**2022.04.15** NAFNet based Stereo Image Super-Resolution solution ([NAFSSR](https://arxiv.org/abs/2204.08714)) won the **1st place** on the NTIRE 2022 Stereo Image Super-resolution Challenge! Training/Evaluation instructions see [here](https://github.com/megvii-research/NAFNet/blob/main/docs/StereoSR.md).
### Installation
This implementation based on [BasicSR](https://github.com/xinntao/BasicSR) which is a open source toolbox for image/video restoration tasks and [HINet](https://github.com/megvii-model/HINet)
```python
python 3.9.5
pytorch 1.11.0
cuda 11.3
```
```
git clone https://github.com/megvii-research/NAFNet
cd NAFNet
pip install -r requirements.txt
python setup.py develop --no_cuda_ext
```
### Quick Start
* Image Denoise Colab Demo: [
](https://colab.research.google.com/drive/1dkO5AyktmBoWwxBwoKFUurIDn0m4qDXT?usp=sharing)
* Image Deblur Colab Demo: [](https://colab.research.google.com/drive/1yR2ClVuMefisH12d_srXMhHnHwwA1YmU?usp=sharing)
* Stereo Image Super-Resolution Colab Demo: [](https://colab.research.google.com/drive/1PkLog2imf7jCOPKq1G32SOISz0eLLJaO?usp=sharing)
* 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/REDS/NAFNet-width64.yml --input_path ./demo/blurry.jpg --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](https://github.com/megvii-research/NAFNet/#results-and-pre-trained-models) should be downloaded.
* Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo for single image restoration[](https://huggingface.co/spaces/chuxiaojie/NAFNet)
* Stereo Image Inference Demo:
* Stereo Image Super-resolution:
```
python basicsr/demo_ssr.py -opt options/test/NAFSSR/NAFSSR-L_4x.yml \
--input_l_path ./demo/lr_img_l.png --input_r_path ./demo/lr_img_r.png \
--output_l_path ./demo/sr_img_l.png --output_r_path ./demo/sr_img_r.png
```
* ```--input_l_path```: the path of the degraded left image
* ```--input_r_path```: the path of the degraded right image
* ```--output_l_path```: the path to save the predicted left image
* ```--output_r_path```: the path to save the predicted right image
* [pretrained models](https://github.com/megvii-research/NAFNet/#results-and-pre-trained-models) should be downloaded.
* Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo for stereo image super-resolution[](https://huggingface.co/spaces/chuxiaojie/NAFSSR)
* Try the web demo with all three tasks here: [](https://replicate.com/megvii-research/nafnet)
### Results and Pre-trained Models
| name | Dataset|PSNR|SSIM| pretrained models | configs |
|:----|:----|:----|:----|:----|-----|
|NAFNet-GoPro-width32|GoPro|32.8705|0.9606|[gdrive](https://drive.google.com/file/d/1Fr2QadtDCEXg6iwWX8OzeZLbHOx2t5Bj/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1AbgG0yoROHmrRQN7dgzDvQ?pwd=so6v)|[train](./options/train/GoPro/NAFNet-width32.yml) \| [test](./options/test/GoPro/NAFNet-width32.yml)|
|NAFNet-GoPro-width64|GoPro|33.7103|0.9668|[gdrive](https://drive.google.com/file/d/1S0PVRbyTakYY9a82kujgZLbMihfNBLfC/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1g-E1x6En-PbYXm94JfI1vg?pwd=wnwh)|[train](./options/train/GoPro/NAFNet-width64.yml) \| [test](./options/test/GoPro/NAFNet-width64.yml)|
|NAFNet-SIDD-width32|SIDD|39.9672|0.9599|[gdrive](https://drive.google.com/file/d/1lsByk21Xw-6aW7epCwOQxvm6HYCQZPHZ/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1Xses38SWl-7wuyuhaGNhaw?pwd=um97)|[train](./options/train/SIDD/NAFNet-width32.yml) \| [test](./options/test/SIDD/NAFNet-width32.yml)|
|NAFNet-SIDD-width64|SIDD|40.3045|0.9614|[gdrive](https://drive.google.com/file/d/14Fht1QQJ2gMlk4N1ERCRuElg8JfjrWWR/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/198kYyVSrY_xZF0jGv9U0sQ?pwd=dton)|[train](./options/train/SIDD/NAFNet-width64.yml) \| [test](./options/test/SIDD/NAFNet-width64.yml)|
|NAFNet-REDS-width64|REDS|29.0903|0.8671|[gdrive](https://drive.google.com/file/d/14D4V4raNYIOhETfcuuLI3bGLB-OYIv6X/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1vg89ccbpIxg3mK9IONBfGg?pwd=9fas)|[train](./options/train/REDS/NAFNet-width64.yml) \| [test](./options/test/REDS/NAFNet-width64.yml)|
|NAFSSR-L_4x|Flickr1024|24.17|0.7589|[gdrive](https://drive.google.com/file/d/1TIdQhPtBrZb2wrBdAp9l8NHINLeExOwb/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1P8ioEuI1gwydA2Avr3nUvw?pwd=qs7a)|[train](./options/train/NAFSSR/NAFSSR-L_4x.yml) \| [test](./options/test/NAFSSR/NAFSSR-L_4x.yml)|
|NAFSSR-L_2x|Flickr1024|29.68|0.9221|[gdrive](https://drive.google.com/file/d/1SZ6bQVYTVS_AXedBEr-_mBCC-qGYHLmf/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1GS6YQSSECH8hAKhvzw6GyQ?pwd=2v3v)|[train](./options/train/NAFSSR/NAFSSR-L_2x.yml) \| [test](./options/test/NAFSSR/NAFSSR-L_2x.yml)|
|Baseline-GoPro-width32|GoPro|32.4799|0.9575|[gdrive](https://drive.google.com/file/d/14z7CxRzVkYEhFgsZg79GlPTEr3VFIGyl/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1WnFKYTAQyAQ9XuD5nlHw_Q?pwd=oieh)|[train](./options/train/GoPro/Baseline-width32.yml) \| [test](./options/test/GoPro/Baseline-width32.yml)|
|Baseline-GoPro-width64|GoPro|33.3960|0.9649|[gdrive](https://drive.google.com/file/d/1yy0oPNJjJxfaEmO0pfPW_TpeoCotYkuO/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1Fqi2T4nyF_wo4wh1QpgIGg?pwd=we36)|[train](./options/train/GoPro/Baseline-width64.yml) \| [test](./options/test/GoPro/Baseline-width64.yml)|
|Baseline-SIDD-width32|SIDD|39.8857|0.9596|[gdrive](https://drive.google.com/file/d/1NhqVcqkDcYvYgF_P4BOOfo9tuTcKDuhW/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1wkskmCRKhXq6dGa6Ns8D0A?pwd=0rin)|[train](./options/train/SIDD/Baseline-width32.yml) \| [test](./options/test/SIDD/Baseline-width32.yml)|
|Baseline-SIDD-width64|SIDD|40.2970|0.9617|[gdrive](https://drive.google.com/file/d/1wQ1HHHPhSp70_ledMBZhDhIGjZQs16wO/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1ivruGfSRGfWq5AEB8qc7YQ?pwd=t9w8)|[train](./options/train/SIDD/Baseline-width64.yml) \| [test](./options/test/SIDD/Baseline-width64.yml)|
### Image Restoration Tasks
| Task | Dataset | Train/Test Instructions | Visualization Results |
| :----------------------------------- | :------ | :---------------------- | :----------------------------------------------------------- |
| Image Deblurring | GoPro | [link](./docs/GoPro.md) | [gdrive](https://drive.google.com/file/d/1S8u4TqQP6eHI81F9yoVR0be-DLh4cNgb/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1yNYQhznChafsbcfHO44aHQ?pwd=96ii)|
| Image Denoising | SIDD | [link](./docs/SIDD.md) | [gdrive](https://drive.google.com/file/d/1rbBYD64bfvbHOrN3HByNg0vz6gHQq7Np/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1wIubY6SeXRfZHpp6bAojqQ?pwd=hu4t)|
| Image Deblurring with JPEG artifacts | REDS | [link](./docs/REDS.md) | [gdrive](https://drive.google.com/file/d/1FwHWYPXdPtUkPqckpz-WBitpVyPuXFRi/view?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/17T30w5xAtBQQ2P3wawLiVA?pwd=put5) |
| Stereo Image Super-Resolution | Flickr1024+Middlebury | [link](./docs/StereoSR.md) | [gdrive](https://drive.google.com/drive/folders/1lTKe2TU7F-KcU-oaF8jqgoUwIMb6RW0w?usp=sharing) \| [百度网盘](https://pan.baidu.com/s/1kov6ivrSFy1FuToCATbyrA?pwd=q263 ) |
### Citations
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 NAFSSR helps your research or work, please consider citing NAFSSR.
```
@InProceedings{chu2022nafssr,
author = {Chu, Xiaojie and Chen, Liangyu and Yu, Wenqing},
title = {NAFSSR: Stereo Image Super-Resolution Using NAFNet},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2022},
pages = {1239-1248}
}
```
### Contact
If you have any questions, please contact chenliangyu@megvii.com or chuxiaojie@megvii.com
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