Simply download Colab-USRNet.ipynb
and open it inside your Google Drive or click here and copy the file with "File > Save a copy to Drive..." into your Google Drive.
Aside USRNet, there are also training and testing codes for DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN and IMDN, but aren't pre-configured in Colab.
Computer Vision Lab, ETH Zurich, Switzerland
- If you can't open
Colab-USRNet.ipynb
inside your Google Drive, try this colab link and save it to your Google Drive. The "open in Colab"-button can be missing in Google Drive, if that person never used Colab. - Google Colab does assign a random GPU. It depends on luck.
- The Google Colab VM does have a maximum session length of 12 hours. Additionally there is a 30 minute timeout if you leave colab. The VM will be deleted after these timeouts.
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 |
- 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
- https://github.com/xinntao/BasicSR/wiki/Prepare-datasets-in-LMDB-format
- set12
- bsd68
- cbsd68
- kodak24
- srbsd68
- set5
- set14
- cbsd100
- urban100
- manga109
@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}
}