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https://github.com/cszn/KAIR

Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR
https://github.com/cszn/KAIR
bsrgan deep-learning denoising dncnn dpsr esrgan ffdnet flops image-restoration pytorch sisr srmd super-resolution swinir toolbox usrnet
Last synced: about 2 months ago
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Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR
Host: GitHub
URL: https://github.com/cszn/KAIR
Owner: cszn
License: mit
Created: 2019-12-15T19:42:25.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2024-04-03T09:43:11.000Z (3 months ago)
Last Synced: 2024-04-03T10:43:10.805Z (3 months ago)
Topics: bsrgan, deep-learning, denoising, dncnn, dpsr, esrgan, ffdnet, flops, image-restoration, pytorch, sisr, srmd, super-resolution, swinir, toolbox, usrnet
Language: Python
Homepage: https://cszn.github.io/
Size: 18.9 MB
Stars: 2,696
Watchers: 45
Forks: 597
Open Issues: 57
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists

my-awesome-stars - cszn/KAIR - Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR (Python)
awesome-stars - cszn/KAIR - Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR (Python)
awesome-computer-vision-resources - [github
awesome-stars - KAIR
README

        ## Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, BSRGAN, SwinIR, VRT, RVRT

[![download](https://img.shields.io/github/downloads/cszn/KAIR/total.svg)](https://github.com/cszn/KAIR/releases) ![visitors](https://visitor-badge.glitch.me/badge?page_id=cszn/KAIR) 

[Kai Zhang](https://cszn.github.io/)

*[Computer Vision Lab](https://vision.ee.ethz.ch/the-institute.html), ETH Zurich, Switzerland*

_______

- **_News (2023-06-02)_**: Code for "[Denoising Diffusion Models for Plug-and-Play Image Restoration](https://github.com/yuanzhi-zhu/DiffPIR)" is released at [yuanzhi-zhu/DiffPIR](https://github.com/yuanzhi-zhu/DiffPIR).

- **_News (2022-10-04)_**: We release [the training codes](https://github.com/cszn/KAIR/blob/master/docs/README_RVRT.md) of [RVRT, NeurlPS2022 ![GitHub Stars](https://img.shields.io/github/stars/JingyunLiang/RVRT?style=social)](https://github.com/JingyunLiang/RVRT) for video SR, deblurring and denoising.

- **_News (2022-05-05)_**: Try the [online demo](https://replicate.com/cszn/scunet) of [SCUNet ![GitHub Stars](https://img.shields.io/github/stars/cszn/SCUNet?style=social)](https://github.com/cszn/SCUNet) for blind real image denoising.

- **_News (2022-03-23)_**: We release [the testing codes](https://github.com/cszn/SCUNet) of [SCUNet ![GitHub Stars](https://img.shields.io/github/stars/cszn/SCUNet?style=social)](https://github.com/cszn/SCUNet) for blind real image denoising.

__*The following results are obtained by our SCUNet with purely synthetic training data! 

We did not use the paired noisy/clean data by DND and SIDD during training!*__

    

- **_News (2022-02-15)_**: We release [the training codes](https://github.com/cszn/KAIR/blob/master/docs/README_VRT.md) of [VRT ![GitHub Stars](https://img.shields.io/github/stars/JingyunLiang/VRT?style=social)](https://github.com/JingyunLiang/VRT) for video SR, deblurring and denoising.

![Eg1](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vsr.gif)

![Eg2](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vdb.gif)

![Eg3](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vdn.gif)

![Eg4](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vfi.gif)

![Eg5](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_stvsr.gif)

- **_News (2021-12-23)_**: Our techniques are adopted in [https://www.amemori.ai/](https://www.amemori.ai/).

- **_News (2021-12-23)_**: Our new work for practical image denoising.

-   

- [](https://imgsli.com/ODczMTc) 

[](https://imgsli.com/ODczMTY) 

- **_News (2021-09-09)_**: Add [main_download_pretrained_models.py](https://github.com/cszn/KAIR/blob/master/main_download_pretrained_models.py) to download pre-trained models.

- **_News (2021-09-08)_**: Add [matlab code](https://github.com/cszn/KAIR/tree/master/matlab) to zoom local part of an image for the purpose of comparison between different results.

- **_News (2021-09-07)_**: We upload [the training code](https://github.com/cszn/KAIR/blob/master/docs/README_SwinIR.md) of [SwinIR ![GitHub Stars](https://img.shields.io/github/stars/JingyunLiang/SwinIR?style=social)](https://github.com/JingyunLiang/SwinIR) and provide an [interactive online Colob demo for real-world image SR](https://colab.research.google.com/gist/JingyunLiang/a5e3e54bc9ef8d7bf594f6fee8208533/swinir-demo-on-real-world-image-sr.ipynb). Try to super-resolve your own images on Colab! 

|Real-World Image (x4)|[BSRGAN, ICCV2021](https://github.com/cszn/BSRGAN)|[Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)|SwinIR (ours)|

|      :---      |     :---:        |        :-----:         |        :-----:         | 

||||

|||||

- **_News (2021-08-31)_**: We upload the [training code of BSRGAN](https://github.com/cszn/BSRGAN#training).

- **_News (2021-08-24)_**: We upload the BSRGAN degradation model.

- **_News (2021-08-22)_**: Support multi-feature-layer VGG perceptual loss and UNet discriminator. 

- **_News (2021-08-18)_**: We upload the extended BSRGAN degradation model. It is slightly different from our published version. 

- **_News (2021-06-03)_**: Add testing codes of [GPEN (CVPR21)](https://github.com/yangxy/GPEN) for face image enhancement: [main_test_face_enhancement.py](https://github.com/cszn/KAIR/blob/master/main_test_face_enhancement.py)

 

 

 

 

 

 

- **_News (2021-05-13)_**: Add [PatchGAN discriminator](https://github.com/cszn/KAIR/blob/master/models/network_discriminator.py).

- **_News (2021-05-12)_**: Support distributed training, see also [https://github.com/xinntao/BasicSR/blob/master/docs/TrainTest.md](https://github.com/xinntao/BasicSR/blob/master/docs/TrainTest.md).

- **_News (2021-01)_**: [BSRGAN](https://github.com/cszn/BSRGAN) for blind real image super-resolution will be added.

- **_Pull requests are welcome!_**

- **Correction (2020-10)**: If you use multiple GPUs for GAN training, remove or comment [Line 105](https://github.com/cszn/KAIR/blob/e52a6944c6a40ba81b88430ffe38fd6517e0449e/models/model_gan.py#L105) to enable `DataParallel` for fast training

- **News (2020-10)**: Add [utils_receptivefield.py](https://github.com/cszn/KAIR/blob/master/utils/utils_receptivefield.py) to calculate receptive field.

- **News (2020-8)**: A `deep plug-and-play image restoration toolbox` is released at [cszn/DPIR](https://github.com/cszn/DPIR).

- **Tips (2020-8)**: Use [this](https://github.com/cszn/KAIR/blob/9fd17abff001ab82a22070f7e442bb5246d2d844/main_challenge_sr.py#L147) to avoid `out of memory` issue.

- **News (2020-7)**: Add [main_challenge_sr.py](https://github.com/cszn/KAIR/blob/23b0d0f717980e48fad02513ba14045d57264fe1/main_challenge_sr.py#L90) to get `FLOPs`, `#Params`, `Runtime`, `#Activations`, `#Conv`, and `Max Memory Allocated`.

```python

from utils.utils_modelsummary import get_model_activation, get_model_flops

input_dim = (3, 256, 256)  # set the input dimension

activations, num_conv2d = get_model_activation(model, input_dim)

logger.info('{:>16s} : {:<.4f} [M]'.format('#Activations', activations/10**6))

logger.info('{:>16s} : {:16s} : {:<.4f} [G]'.format('FLOPs', flops/10**9))

num_parameters = sum(map(lambda x: x.numel(), model.parameters()))

logger.info('{:>16s} : {:<.4f} [M]'.format('#Params', num_parameters/10**6))

```

- **News (2020-6)**: Add [USRNet (CVPR 2020)](https://github.com/cszn/USRNet) for training and testing.

  - [Network Architecture](https://github.com/cszn/KAIR/blob/3357aa0e54b81b1e26ceb1cee990f39add235e17/models/network_usrnet.py#L309)

  - [Dataset](https://github.com/cszn/KAIR/blob/6c852636d3715bb281637863822a42c72739122a/data/dataset_usrnet.py#L16)

Clone repo

----------

```

git clone https://github.com/cszn/KAIR.git

```

```

pip install -r requirement.txt

```

Training

----------

You should modify the json file from [options](https://github.com/cszn/KAIR/tree/master/options) first, for example,

setting ["gpu_ids": [0,1,2,3]](https://github.com/cszn/KAIR/blob/ff80d265f64de67dfb3ffa9beff8949773c81a3d/options/train_msrresnet_psnr.json#L4) if 4 GPUs are used,

setting ["dataroot_H": "trainsets/trainH"](https://github.com/cszn/KAIR/blob/ff80d265f64de67dfb3ffa9beff8949773c81a3d/options/train_msrresnet_psnr.json#L24) if path of the high quality dataset is `trainsets/trainH`.

- Training with `DataParallel` - PSNR

```python

python main_train_psnr.py --opt options/train_msrresnet_psnr.json

```

- Training with `DataParallel` - GAN

```python

python main_train_gan.py --opt options/train_msrresnet_gan.json

```

- Training with `DistributedDataParallel` - PSNR - 4 GPUs

```python

python -m torch.distributed.launch --nproc_per_node=4 --master_port=1234 main_train_psnr.py --opt options/train_msrresnet_psnr.json  --dist True

```

- Training with `DistributedDataParallel` - PSNR - 8 GPUs

```python

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_psnr.py --opt options/train_msrresnet_psnr.json  --dist True

```

- Training with `DistributedDataParallel` - GAN - 4 GPUs

```python

python -m torch.distributed.launch --nproc_per_node=4 --master_port=1234 main_train_gan.py --opt options/train_msrresnet_gan.json  --dist True

```

- Training with `DistributedDataParallel` - GAN - 8 GPUs

```python

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_gan.py --opt options/train_msrresnet_gan.json  --dist True

```

- Kill distributed training processes of `main_train_gan.py`

```python

kill $(ps aux | grep main_train_gan.py | grep -v grep | awk '{print $2}')

```

----------

| Method | Original Link |

|---|---|

| DnCNN |[https://github.com/cszn/DnCNN](https://github.com/cszn/DnCNN)|

| FDnCNN |[https://github.com/cszn/DnCNN](https://github.com/cszn/DnCNN)|

| FFDNet | [https://github.com/cszn/FFDNet](https://github.com/cszn/FFDNet)|

| SRMD | [https://github.com/cszn/SRMD](https://github.com/cszn/SRMD)|

| DPSR-SRResNet | [https://github.com/cszn/DPSR](https://github.com/cszn/DPSR)|

| SRResNet | [https://github.com/xinntao/BasicSR](https://github.com/xinntao/BasicSR)|

| ESRGAN | [https://github.com/xinntao/ESRGAN](https://github.com/xinntao/ESRGAN)|

| RRDB | [https://github.com/xinntao/ESRGAN](https://github.com/xinntao/ESRGAN)|

| IMDB | [https://github.com/Zheng222/IMDN](https://github.com/Zheng222/IMDN)|

| USRNet | [https://github.com/cszn/USRNet](https://github.com/cszn/USRNet)|

| DRUNet | [https://github.com/cszn/DPIR](https://github.com/cszn/DPIR)|

| DPIR | [https://github.com/cszn/DPIR](https://github.com/cszn/DPIR)|

| BSRGAN | [https://github.com/cszn/BSRGAN](https://github.com/cszn/BSRGAN)|

| SwinIR | [https://github.com/JingyunLiang/SwinIR](https://github.com/JingyunLiang/SwinIR)|

| VRT | [https://github.com/JingyunLiang/VRT](https://github.com/JingyunLiang/VRT)       |

| DiffPIR | [https://github.com/yuanzhi-zhu/DiffPIR](https://github.com/yuanzhi-zhu/DiffPIR)|

Network architectures

----------

* [USRNet](https://github.com/cszn/USRNet)

   

* DnCNN

   

 

* IRCNN denoiser

  

* FFDNet

   

* SRMD

   

* SRResNet, SRGAN, RRDB, ESRGAN

   

  

* IMDN

    -----  

Testing

----------

|Method | [model_zoo](model_zoo)|

|---|---|

| [main_test_dncnn.py](main_test_dncnn.py) |```dncnn_15.pth, dncnn_25.pth, dncnn_50.pth, dncnn_gray_blind.pth, dncnn_color_blind.pth, dncnn3.pth```|

| [main_test_ircnn_denoiser.py](main_test_ircnn_denoiser.py) | ```ircnn_gray.pth, ircnn_color.pth```| 

| [main_test_fdncnn.py](main_test_fdncnn.py) | ```fdncnn_gray.pth, fdncnn_color.pth, fdncnn_gray_clip.pth, fdncnn_color_clip.pth```|

| [main_test_ffdnet.py](main_test_ffdnet.py) | ```ffdnet_gray.pth, ffdnet_color.pth, ffdnet_gray_clip.pth, ffdnet_color_clip.pth```|

| [main_test_srmd.py](main_test_srmd.py) | ```srmdnf_x2.pth, srmdnf_x3.pth, srmdnf_x4.pth, srmd_x2.pth, srmd_x3.pth, srmd_x4.pth```| 

|  | **The above models are converted from MatConvNet.** |

| [main_test_dpsr.py](main_test_dpsr.py) | ```dpsr_x2.pth, dpsr_x3.pth, dpsr_x4.pth, dpsr_x4_gan.pth```|

| [main_test_msrresnet.py](main_test_msrresnet.py) | ```msrresnet_x4_psnr.pth, msrresnet_x4_gan.pth```|

| [main_test_rrdb.py](main_test_rrdb.py) | ```rrdb_x4_psnr.pth, rrdb_x4_esrgan.pth```|

| [main_test_imdn.py](main_test_imdn.py) | ```imdn_x4.pth```|

[model_zoo](model_zoo)

--------

- download link [https://drive.google.com/drive/folders/13kfr3qny7S2xwG9h7v95F5mkWs0OmU0D](https://drive.google.com/drive/folders/13kfr3qny7S2xwG9h7v95F5mkWs0OmU0D)

[trainsets](trainsets)

----------

- [https://github.com/xinntao/BasicSR/blob/master/docs/DatasetPreparation.md](https://github.com/xinntao/BasicSR/blob/master/docs/DatasetPreparation.md)

- [train400](https://github.com/cszn/DnCNN/tree/master/TrainingCodes/DnCNN_TrainingCodes_v1.0/data)

- [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/)

- [Flickr2K](https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar)

- optional: use [split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=512, p_overlap=96, p_max=800)](https://github.com/cszn/KAIR/blob/3ee0bf3e07b90ec0b7302d97ee2adb780617e637/utils/utils_image.py#L123) to get ```trainsets/trainH``` with small images for fast data loading

[testsets](testsets)

-----------

- [https://github.com/xinntao/BasicSR/blob/master/docs/DatasetPreparation.md](https://github.com/xinntao/BasicSR/blob/master/docs/DatasetPreparation.md)

- [set12](https://github.com/cszn/FFDNet/tree/master/testsets)

- [bsd68](https://github.com/cszn/FFDNet/tree/master/testsets)

- [cbsd68](https://github.com/cszn/FFDNet/tree/master/testsets)

- [kodak24](https://github.com/cszn/FFDNet/tree/master/testsets)

- [srbsd68](https://github.com/cszn/DPSR/tree/master/testsets/BSD68/GT)

- set5

- set14

- cbsd100

- urban100

- manga109

References

----------

```BibTex

@inproceedings{zhu2023denoising, % DiffPIR

title={Denoising Diffusion Models for Plug-and-Play Image Restoration},

author={Yuanzhi Zhu and Kai Zhang and Jingyun Liang and Jiezhang Cao and Bihan Wen and Radu Timofte and Luc Van Gool},

booktitle={IEEE Conference on Computer Vision and Pattern Recognition Workshops},

year={2023}

}

@article{liang2022vrt,

title={VRT: A Video Restoration Transformer},

author={Liang, Jingyun and Cao, Jiezhang and Fan, Yuchen and Zhang, Kai and Ranjan, Rakesh and Li, Yawei and Timofte, Radu and Van Gool, Luc},

journal={arXiv preprint arXiv:2022.00000},

year={2022}

}

@inproceedings{liang2021swinir,

title={SwinIR: Image Restoration Using Swin Transformer},

author={Liang, Jingyun and Cao, Jiezhang and Sun, Guolei and Zhang, Kai and Van Gool, Luc and Timofte, Radu},

booktitle={IEEE International Conference on Computer Vision Workshops},

pages={1833--1844},

year={2021}

}

@inproceedings{zhang2021designing,

title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution},

author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu},

booktitle={IEEE International Conference on Computer Vision},

pages={4791--4800},

year={2021}

}

@article{zhang2021plug, % DPIR & DRUNet & IRCNN

  title={Plug-and-Play Image Restoration with Deep Denoiser Prior},

  author={Zhang, Kai and Li, Yawei and Zuo, Wangmeng and Zhang, Lei and Van Gool, Luc and Timofte, Radu},

  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},

  year={2021}

}

@inproceedings{zhang2020aim, % efficientSR_challenge

  title={AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results},

  author={Kai Zhang and Martin Danelljan and Yawei Li and Radu Timofte and others},

  booktitle={European Conference on Computer Vision Workshops},

  year={2020}

}

@inproceedings{zhang2020deep, % USRNet

  title={Deep unfolding network for image super-resolution},

  author={Zhang, Kai and Van Gool, Luc and Timofte, Radu},

  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},

  pages={3217--3226},

  year={2020}

}

@article{zhang2017beyond, % DnCNN

  title={Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising},

  author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},

  journal={IEEE Transactions on Image Processing},

  volume={26},

  number={7},

  pages={3142--3155},

  year={2017}

}

@inproceedings{zhang2017learning, % IRCNN

title={Learning deep CNN denoiser prior for image restoration},

author={Zhang, Kai and Zuo, Wangmeng and Gu, Shuhang and Zhang, Lei},

booktitle={IEEE conference on computer vision and pattern recognition},

pages={3929--3938},

year={2017}

}

@article{zhang2018ffdnet, % FFDNet, FDnCNN

  title={FFDNet: Toward a fast and flexible solution for CNN-based image denoising},

  author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},

  journal={IEEE Transactions on Image Processing},

  volume={27},

  number={9},

  pages={4608--4622},

  year={2018}

}

@inproceedings{zhang2018learning, % SRMD

  title={Learning a single convolutional super-resolution network for multiple degradations},

  author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},

  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},

  pages={3262--3271},

  year={2018}

}

@inproceedings{zhang2019deep, % DPSR

  title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},

  author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},

  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},

  pages={1671--1681},

  year={2019}

}

@InProceedings{wang2018esrgan, % ESRGAN, MSRResNet

    author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change},

    title = {ESRGAN: Enhanced super-resolution generative adversarial networks},

    booktitle = {The European Conference on Computer Vision Workshops (ECCVW)},

    month = {September},

    year = {2018}

}

@inproceedings{hui2019lightweight, % IMDN

  title={Lightweight Image Super-Resolution with Information Multi-distillation Network},

  author={Hui, Zheng and Gao, Xinbo and Yang, Yunchu and Wang, Xiumei},

  booktitle={Proceedings of the 27th ACM International Conference on Multimedia (ACM MM)},

  pages={2024--2032},

  year={2019}

}

@inproceedings{zhang2019aim, % IMDN

  title={AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results},

  author={Kai Zhang and Shuhang Gu and Radu Timofte and others},

  booktitle={IEEE International Conference on Computer Vision Workshops},

  year={2019}

}

@inproceedings{yang2021gan,

    title={GAN Prior Embedded Network for Blind Face Restoration in the Wild},

    author={Tao Yang, Peiran Ren, Xuansong Xie, and Lei Zhang},

    booktitle={IEEE Conference on Computer Vision and Pattern Recognition},

    year={2021}

}

```