Awesome
<!-- The official pytorch implementation of the paper **[Simple Baselines for Image Restoration (ECCV2022)](https://arxiv.org/abs/2204.04676)** -->Robust Image Denoising through Adversarial Frequency Mixup (2024 CVPR) [paper]
Installation
This implementation based on BasicSR which is a open source toolbox for image/video restoration tasks and NAFNet
python 3.8.8
pytorch 1.9.0
cuda 11.3
git clone https://github.com/dhryougit/AFM.git
cd AFM
pip install -r requirements.txt
python setup.py develop --no_cuda_ext
We used NVIDIA RTX A6000 D6 48GB for trianing our models.
QuickStart
For training
python3 -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/train/DnCNN.yml --name=DnCNN-afm-b --afm_type=AFM_B --seed=10 --afm_rate=0.8 --afm_easy_rate=0.3 --launcher pytorch
Dataset
Training dataset : SIDD
Evaludation datasets : Poly, CC, HighISO, iPhone, Huawei.
Additioanl real-world noise datasets can be downloaded from "https://github.com/ZhaomingKong/Denoising-Comparison"
Results and Pre-trained model
| Dataset | Poly | CC | HighISO | iPhone | Huawei | OOD Avg. |
|---|---|---|---|---|---|---|
| PSRN | 37.75 | 36.84 | 39.17 | 40.65 | 38.39 | 38.56 |
| SSIM | 0.9804 | 0.9830 | 0.9801 | 0.9777 | 0.9683 | 0.9779 |
Pre-trained model of our Dncnn trained on AFM-B can be downloaded from (https://drive.google.com/file/d/1uPJP2zNc4ViFc1QU7TXGAwrFIEScBJvL/view?usp=sharing)
<!-- * Image Denoise Colab Demo: [<a href="https://colab.research.google.com/drive/1dkO5AyktmBoWwxBwoKFUurIDn0m4qDXT?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/drive/1dkO5AyktmBoWwxBwoKFUurIDn0m4qDXT?usp=sharing) * Image Deblur Colab Demo: [<a href="https://colab.research.google.com/drive/1yR2ClVuMefisH12d_srXMhHnHwwA1YmU?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/drive/1yR2ClVuMefisH12d_srXMhHnHwwA1YmU?usp=sharing) * Stereo Image Super-Resolution Colab Demo: [<a href="https://colab.research.google.com/drive/1PkLog2imf7jCOPKq1G32SOISz0eLLJaO?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](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/test/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/test/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 --- <details> <summary>statistics</summary>  </details> -->