Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, BSRGAN, SwinIR
Computer Vision Lab, ETH Zurich, Switzerland
- News (2021-09-09): Add main_download_pretrained_models.py to download pre-trained models.
- News (2021-09-08): Add matlab code to zoom local part of an image for the purpose of comparison between different results.
- 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!
| Real-World Image (x4) | BSRGAN, ICCV2021 | Real-ESRGAN | SwinIR (ours) |
|---|---|---|---|
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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
DataParallelfor 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 toolboxis released at cszn/DPIR. -
Tips (2020-8): Use this to avoid
out of memoryissue. -
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} : {:
- 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}')
| 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 |
Network architectures
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DnCNN
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IRCNN denoiser
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FFDNet
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SRMD
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SRResNet, SRGAN, RRDB, ESRGAN
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IMDN
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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/trainHwith 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{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},
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},
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}
}
My results were shown above, the blue ones are results in the paper. I can get the same results using the pretrained model download from drive. The red ones are my results which have a large gap between the ones in papers.
I run the code of utils_blindsr.py in /utils,I produce this image which have much unrealistic.How this happen and how constrained the produce image like the following:
thanks!
80 Dec 8, 2022
3 Apr 26, 2022
906 Dec 30, 2022
84 Dec 9, 2022
12 May 29, 2021
37 Nov 27, 2022
22 Dec 17, 2022
303 Dec 31, 2022
88 Nov 7, 2022
69 Dec 15, 2022
17.2k Jan 2, 2023
7.4k Jan 1, 2023
497 Dec 22, 2022
11 Jun 19, 2022
11.3k Dec 30, 2022
615 Dec 8, 2022
3 Nov 30, 2021