Awesome
Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, BSRGAN, SwinIR, VRT, RVRT
Computer Vision Lab, ETH Zurich, Switzerland
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News (2023-06-02): Code for "Denoising Diffusion Models for Plug-and-Play Image Restoration" is released at yuanzhi-zhu/DiffPIR.
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News (2022-10-04): We release the training codes of RVRT, NeurlPS2022
for video SR, deblurring and denoising.
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News (2022-05-05): Try the online demo of SCUNet
for blind real image denoising.
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News (2022-03-23): We release the testing codes of 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!
<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"/>
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News (2022-02-15): We release the training codes of VRT
for video SR, deblurring and denoising.
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News (2021-12-23): Our techniques are adopted in https://www.amemori.ai/.
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News (2021-12-23): Our new work for practical image denoising.
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<img src="figs/palace.png" height="320px"/> <img src="figs/palace_HSCU.png" height="320px"/>
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<img src="https://github.com/cszn/KAIR/raw/master/figs/denoising_02.png" height="256px"/> <img src="https://github.com/cszn/KAIR/raw/master/figs/denoising_01.png" height="256px"/>
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News (2021-09-09): Add main_download_pretrained_models.py to download pre-trained models.
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News (2021-09-08): Add matlab code to zoom local part of an image for the purpose of comparison between different results.
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News (2021-09-07): We upload the training code of SwinIR
and provide an interactive online Colob demo for real-world image SR. Try to super-resolve your own images on Colab! <a href="https://colab.research.google.com/gist/JingyunLiang/a5e3e54bc9ef8d7bf594f6fee8208533/swinir-demo-on-real-world-image-sr.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
Real-World Image (x4) | BSRGAN, ICCV2021 | Real-ESRGAN | SwinIR (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"> |
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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
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News (2021-05-13): Add PatchGAN discriminator.
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News (2021-05-12): Support distributed training, see also https://github.com/xinntao/BasicSR/blob/master/docs/TrainTest.md.
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News (2021-01): BSRGAN for blind real image super-resolution will be added.
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Pull requests are welcome!
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Correction (2020-10): If you use multiple GPUs for GAN training, remove or comment Line 105 to enable
DataParallel
for fast training -
News (2020-10): Add utils_receptivefield.py to calculate receptive field.
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News (2020-8): A
deep plug-and-play image restoration toolbox
is released at cszn/DPIR. -
Tips (2020-8): Use this to avoid
out of memory
issue. -
News (2020-7): Add main_challenge_sr.py to get
FLOPs
,#Params
,Runtime
,#Activations
,#Conv
, andMax Memory Allocated
.
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))
- News (2020-6): Add USRNet (CVPR 2020) for training and testing.
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
- <img src="https://github.com/cszn/USRNet/blob/master/figs/architecture.png" width="600px"/>
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DnCNN
<img src="https://github.com/cszn/DnCNN/blob/master/figs/dncnn.png" width="600px"/> -
IRCNN denoiser
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FFDNet
<img src="https://github.com/cszn/FFDNet/blob/master/figs/ffdnet.png" width="600px"/> -
SRMD
<img src="https://github.com/cszn/SRMD/blob/master/figs/architecture.png" width="605px"/> -
SRResNet, SRGAN, RRDB, ESRGAN
<img src="https://github.com/xinntao/ESRGAN/blob/master/figures/architecture.jpg" width="595px"/> -
IMDN
<img src="figs/imdn.png" width="460px"/> ----- <img src="figs/imdn_block.png" width="100px"/>
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
- https://github.com/xinntao/BasicSR/blob/master/docs/DatasetPreparation.md
- train400
- DIV2K
- Flickr2K
- optional: use split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=512, p_overlap=96, p_max=800) to get
trainsets/trainH
with small images for fast data loading
testsets
- https://github.com/xinntao/BasicSR/blob/master/docs/DatasetPreparation.md
- set12
- bsd68
- cbsd68
- kodak24
- srbsd68
- set5
- set14
- cbsd100
- urban100
- manga109
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}
}