/KAIR

Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR

Primary LanguagePythonMIT LicenseMIT

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

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Kai Zhang

Computer Vision Lab, ETH Zurich, Switzerland

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!

Real-World Image (x4) BSRGAN, ICCV2021 Real-ESRGAN SwinIR (ours)

  • News (2021-08-31): We upload the training code of BSRGAN.

  • 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) for face image enhancement: main_test_face_enhancement.py

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} : {:<d}'.format('#Conv2d', num_conv2d))
flops = get_model_flops(model, input_dim, False)
logger.info('{:>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))

Clone repo

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

pip install -r requirement.txt

Training

You should modify the json file from options first, for example, setting "gpu_ids": [0,1,2,3] if 4 GPUs are used, setting "dataroot_H": "trainsets/trainH" if path of the high quality dataset is trainsets/trainH.

  • Training with DataParallel - PSNR
python main_train_psnr.py --opt options/train_msrresnet_psnr.json
  • Training with DataParallel - GAN
python main_train_gan.py --opt options/train_msrresnet_gan.json
  • Training with DistributedDataParallel - PSNR - 4 GPUs
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 -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 -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 -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
kill $(ps aux | grep main_train_gan.py | grep -v grep | awk '{print $2}')
Method Original Link
DnCNN https://github.com/cszn/DnCNN
FDnCNN https://github.com/cszn/DnCNN
FFDNet https://github.com/cszn/FFDNet
SRMD https://github.com/cszn/SRMD
DPSR-SRResNet https://github.com/cszn/DPSR
SRResNet https://github.com/xinntao/BasicSR
ESRGAN https://github.com/xinntao/ESRGAN
RRDB https://github.com/xinntao/ESRGAN
IMDB https://github.com/Zheng222/IMDN
USRNet https://github.com/cszn/USRNet
DRUNet https://github.com/cszn/DPIR
DPIR https://github.com/cszn/DPIR
BSRGAN https://github.com/cszn/BSRGAN
SwinIR https://github.com/JingyunLiang/SwinIR
VRT https://github.com/JingyunLiang/VRT

Network architectures

  • FFDNet

  • SRMD

  • SRResNet, SRGAN, RRDB, ESRGAN

  • IMDN

    -----

Testing

Method model_zoo
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 ircnn_gray.pth, ircnn_color.pth
main_test_fdncnn.py fdncnn_gray.pth, fdncnn_color.pth, fdncnn_gray_clip.pth, fdncnn_color_clip.pth
main_test_ffdnet.py ffdnet_gray.pth, ffdnet_color.pth, ffdnet_gray_clip.pth, ffdnet_color_clip.pth
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 dpsr_x2.pth, dpsr_x3.pth, dpsr_x4.pth, dpsr_x4_gan.pth
main_test_msrresnet.py msrresnet_x4_psnr.pth, msrresnet_x4_gan.pth
main_test_rrdb.py rrdb_x4_psnr.pth, rrdb_x4_esrgan.pth
main_test_imdn.py imdn_x4.pth

model_zoo

trainsets

testsets

References

@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}
}