Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, BSRGAN, SwinIR, VRT, RVRT
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!
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News (2021-08-31): We upload the training code of BSRGAN.
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News (2021-08-24): We upload the BSRGAN degradation model.
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News (2021-08-22): Support multi-feature-layer VGG perceptual loss and UNet discriminator.
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News (2021-08-18): We upload the extended BSRGAN degradation model. It is slightly different from our published version.
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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}')
Network architectures
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 |
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}
}
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