News: USRNet (CVPR 2020) will be added.
- 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
- 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
- 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={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}
}