GitHub - cszn/KAIR: Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR
Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, BSRGAN, SwinIR, VRT, RVRT
The following results are obtained by our SCUNet with purely synthetic training data! We did not use the paired noisy/clean data by DND and SIDD during training!
@inproceedings{zhu2023denoising, % DiffPIR title={Denoising Diffusion Models for Plug-and-Play Image Restoration}, author={Yuanzhi Zhu and Kai Zhang and Jingyun Liang and Jiezhang Cao and Bihan Wen and Radu Timofte and Luc Van Gool}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition Workshops}, year={2023} } @article{liang2022vrt, title={VRT: A Video Restoration Transformer}, author={Liang, Jingyun and Cao, Jiezhang and Fan, Yuchen and Zhang, Kai and Ranjan, Rakesh and Li, Yawei and Timofte, Radu and Van Gool, Luc}, journal={arXiv preprint arXiv:2022.00000}, year={2022} } @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}, pages={1833--1844}, 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}, pages={4791--4800}, 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} }