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

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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!

<img src="https://github.com/cszn/cszn.github.io/blob/master/files/input_16.gif" width="360px"/> <img src="https://github.com/cszn/cszn.github.io/blob/master/files/wm_fnb_0010_16.gif" width="360px"/>

Real-World Image (x4)BSRGAN, ICCV2021Real-ESRGANSwinIR (ours)
<img width="200" src="https://raw.githubusercontent.com/JingyunLiang/SwinIR/main/figs/ETH_LR.png"><img width="200" src="https://raw.githubusercontent.com/JingyunLiang/SwinIR/main/figs/ETH_BSRGAN.png"><img width="200" src="https://raw.githubusercontent.com/JingyunLiang/SwinIR/main/figs/ETH_realESRGAN.jpg"><img width="200" src="https://raw.githubusercontent.com/JingyunLiang/SwinIR/main/figs/ETH_SwinIR.png">
<img width="200" src="https://raw.githubusercontent.com/JingyunLiang/SwinIR/main/figs/OST_009_crop_LR.png"><img width="200" src="https://raw.githubusercontent.com/JingyunLiang/SwinIR/main/figs/OST_009_crop_BSRGAN.png"><img width="200" src="https://raw.githubusercontent.com/JingyunLiang/SwinIR/main/figs/OST_009_crop_realESRGAN.png"><img width="200" src="https://raw.githubusercontent.com/JingyunLiang/SwinIR/main/figs/OST_009_crop_SwinIR.png">
<img src="figs/face_04_comparison.png" width="730px"/> <img src="figs/face_13_comparison.png" width="730px"/> <img src="figs/face_08_comparison.png" width="730px"/> <img src="figs/face_01_comparison.png" width="730px"/> <img src="figs/face_12_comparison.png" width="730px"/> <img src="figs/face_10_comparison.png" width="730px"/> 
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.

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

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

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

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

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

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 $(ps aux | grep main_train_gan.py | grep -v grep | awk '{print $2}')
MethodOriginal Link
DnCNNhttps://github.com/cszn/DnCNN
FDnCNNhttps://github.com/cszn/DnCNN
FFDNethttps://github.com/cszn/FFDNet
SRMDhttps://github.com/cszn/SRMD
DPSR-SRResNethttps://github.com/cszn/DPSR
SRResNethttps://github.com/xinntao/BasicSR
ESRGANhttps://github.com/xinntao/ESRGAN
RRDBhttps://github.com/xinntao/ESRGAN
IMDBhttps://github.com/Zheng222/IMDN
USRNethttps://github.com/cszn/USRNet
DRUNethttps://github.com/cszn/DPIR
DPIRhttps://github.com/cszn/DPIR
BSRGANhttps://github.com/cszn/BSRGAN
SwinIRhttps://github.com/JingyunLiang/SwinIR
VRThttps://github.com/JingyunLiang/VRT
DiffPIRhttps://github.com/yuanzhi-zhu/DiffPIR

Network architectures

<img src="https://github.com/lipengFu/IRCNN/raw/master/Image/image_2.png" width="680px"/>

Testing

Methodmodel_zoo
main_test_dncnn.pydncnn_15.pth, dncnn_25.pth, dncnn_50.pth, dncnn_gray_blind.pth, dncnn_color_blind.pth, dncnn3.pth
main_test_ircnn_denoiser.pyircnn_gray.pth, ircnn_color.pth
main_test_fdncnn.pyfdncnn_gray.pth, fdncnn_color.pth, fdncnn_gray_clip.pth, fdncnn_color_clip.pth
main_test_ffdnet.pyffdnet_gray.pth, ffdnet_color.pth, ffdnet_gray_clip.pth, ffdnet_color_clip.pth
main_test_srmd.pysrmdnf_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.pydpsr_x2.pth, dpsr_x3.pth, dpsr_x4.pth, dpsr_x4_gan.pth
main_test_msrresnet.pymsrresnet_x4_psnr.pth, msrresnet_x4_gan.pth
main_test_rrdb.pyrrdb_x4_psnr.pth, rrdb_x4_esrgan.pth
main_test_imdn.pyimdn_x4.pth

model_zoo

trainsets

testsets

References

@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}
}