| Weighted nuclear norm minimization with application to image denoising S Gu, L Zhang, W Zuo, X Feng Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 2436 | 2014 |
| Learning deep CNN denoiser prior for image restoration K Zhang, W Zuo, S Gu, L Zhang Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 2249 | 2017 |
| Ntire 2017 challenge on single image super-resolution: Methods and results R Timofte, E Agustsson, L Van Gool, MH Yang, L Zhang Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 2065 | 2017 |
| Learning a deep single image contrast enhancer from multi-exposure images J Cai, S Gu, L Zhang IEEE Transactions on Image Processing 27 (4), 2049-2062, 2018 | 950 | 2018 |
| Weighted nuclear norm minimization and its applications to low level vision S Gu, Q Xie, D Meng, W Zuo, X Feng, L Zhang International journal of computer vision 121, 183-208, 2017 | 820 | 2017 |
| Learning convolutional networks for content-weighted image compression M Li, W Zuo, S Gu, D Zhao, D Zhang Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 467 | 2018 |
| Convolutional sparse coding for image super-resolution S Gu, W Zuo, Q Xie, D Meng, X Feng, L Zhang Proceedings of the IEEE International Conference on Computer Vision, 1823-1831, 2015 | 423 | 2015 |
| Weighted Schatten-Norm Minimization for Image Denoising and Background Subtraction Y Xie, S Gu, Y Liu, W Zuo, W Zhang, L Zhang IEEE transactions on image processing 25 (10), 4842-4857, 2016 | 416 | 2016 |
| Projective dictionary pair learning for pattern classification S Gu, L Zhang, W Zuo, X Feng Advances in neural information processing systems 27, 2014 | 374 | 2014 |
| Multispectral images denoising by intrinsic tensor sparsity regularization Q Xie, Q Zhao, D Meng, Z Xu, S Gu, W Zuo, L Zhang Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 263 | 2016 |
| Group sparsity: The hinge between filter pruning and decomposition for network compression Y Li, S Gu, C Mayer, LV Gool, R Timofte Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 255 | 2020 |
| Joint convolutional analysis and synthesis sparse representation for single image layer separation S Gu, D Meng, W Zuo, L Zhang Proceedings of the IEEE international conference on computer vision, 1708-1716, 2017 | 244 | 2017 |
| Video super-resolution with recurrent structure-detail network T Isobe, X Jia, S Gu, S Li, S Wang, Q Tian Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 238 | 2020 |
| Frequency separation for real-world super-resolution M Fritsche, S Gu, R Timofte 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW …, 2019 | 236 | 2019 |
| Video rain streak removal by multiscale convolutional sparse coding M Li, Q Xie, Q Zhao, W Wei, S Gu, J Tao, D Meng Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 215 | 2018 |
| You only look yourself: Unsupervised and untrained single image dehazing neural network B Li, Y Gou, S Gu, JZ Liu, JT Zhou, X Peng International Journal of Computer Vision 129, 1754-1767, 2021 | 191 | 2021 |
| Self-guided network for fast image denoising S Gu, Y Li, LV Gool, R Timofte Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 186 | 2019 |
| Flow-based kernel prior with application to blind super-resolution J Liang, K Zhang, S Gu, L Van Gool, R Timofte Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 152 | 2021 |
| Efficient video super-resolution through recurrent latent space propagation D Fuoli, S Gu, R Timofte 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW …, 2019 | 146 | 2019 |
| Unsupervised real-world image super resolution via domain-distance aware training Y Wei, S Gu, Y Li, R Timofte, L Jin, H Song Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 145 | 2021 |
Radu TimofteHumboldt Professor for AI and Computer Vision, University of WürzburgVerified email at uni-wuerzburg.de
