Giters

skeeet / KAIR

Image Restoration Toolbox (PyTorch). Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, IMDN

Home Page:https://cszn.github.io/

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, IMDN

Kai Zhang

Computer Vision Lab, ETH Zurich, Switzerland

  • Pull requests are welcome!

  • 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, and Max 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))

Training

Method Original Link
main_train_dncnn.py https://github.com/cszn/DnCNN
main_train_fdncnn.py https://github.com/cszn/DnCNN
main_train_ffdnet.py https://github.com/cszn/FFDNet
main_train_srmd.py https://github.com/cszn/SRMD
main_train_dpsr.py https://github.com/cszn/DPSR
main_train_msrresnet_psnr.py https://github.com/xinntao/BasicSR
main_train_msrresnet_gan.py https://github.com/xinntao/ESRGAN
main_train_rrdb_psnr.py https://github.com/xinntao/ESRGAN
main_train_imdn.py https://github.com/Zheng222/IMDN

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

@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}
}

About

Image Restoration Toolbox (PyTorch). Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, IMDN

https://cszn.github.io/

License:MIT License

Languages

Language:Python 100.0%