cszn/KAIR

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

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

News: USRNet (CVPR 2020) will be added.

Training

• 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

• USRNet

#TODO

• DnCNN

• FFDNet

• SRMD

• SRResNet, SRGAN, RRDB, ESRGAN

• IMDN

  —–

Testing

• main_test_dncnn.py —————> model_zoo: dncnn_15.pth, dncnn_25.pth, dncnn_50.pth, dncnn_gray_blind.pth, dncnn_color_blind.pth, dncnn3.pth
• main_test_fdncnn.py ————–> model_zoo: fdncnn_gray.pth, fdncnn_color.pth, fdncnn_gray_clip.pth, fdncnn_color_clip.pth
• main_test_ffdnet.py —————> model_zoo: ffdnet_gray.pth, ffdnet_color.pth, ffdnet_gray_clip.pth, ffdnet_color_clip.pth
• main_test_srmd.py —————-> model_zoo: 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 —————–> model_zoo: dpsr_x2.pth, dpsr_x3.pth, dpsr_x4.pth, dpsr_x4_gan.pth
• main_test_msrresnet.py ———–> model_zoo: msrresnet_x4_psnr.pth, msrresnet_x4_gan.pth
• main_test_rrdb.py —————–> model_zoo: rrdb_x4_psnr.pth, rrdb_x4_esrgan.pth
• main_test_imdn.py —————-> model_zoo: imdn_x4.pth

model_zoo

• download link https://drive.google.com/drive/folders/13kfr3qny7S2xwG9h7v95F5mkWs0OmU0D

trainsets

• https://github.com/xinntao/BasicSR/wiki/Prepare-datasets-in-LMDB-format
• 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/wiki/Prepare-datasets-in-LMDB-format
• set12
• bsd68
• cbsd68
• kodak24
• srbsd68
• set5
• set14
• cbsd100
• urban100
• manga109

References

@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={0--0},
  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}
}
@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}
}