| Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising K Zhang, W Zuo, Y Chen, D Meng, L Zhang IEEE TIP 2017, code: https://github.com/cszn/KAIR, 2017 | 7050 | 2017 |
| FFDNet: Toward a fast and flexible solution for CNN based image denoising K Zhang, W Zuo, L Zhang IEEE TIP 2018, code: https://github.com/cszn/KAIR, 2018 | 2014 | 2018 |
| Learning Deep CNN Denoiser Prior for Image Restoration K Zhang, W Zuo, S Gu, L Zhang CVPR 2017, code: https://github.com/cszn/DPIR, 2017 | 1942 | 2017 |
| SwinIR: Image Restoration using Swin Transformer J Liang, J Cao, G Sun, K Zhang*(Corresponding author), L Van Gool, ... ICCV Workshops; code: https://github.com/JingyunLiang/SwinIR, 2021 | 1492 | 2021 |
| Ntire 2017 challenge on single image super-resolution: Methods and results R Timofte, E Agustsson, L Van Gool, MH Yang, L Zhang, B Lim, S Son, ... CVPRW 2017, 2017 | 1490 | 2017 |
| Learning a single convolutional super-resolution network for multiple degradations K Zhang, W Zuo, L Zhang CVPR 2018, code: https://github.com/cszn/KAIR, 2018 | 976 | 2018 |
| Toward convolutional blind denoising of real photographs S Guo, Z Yan, K Zhang, W Zuo, L Zhang CVPR 2019, 2019 | 921 | 2019 |
| Multi-level wavelet-CNN for image restoration P Liu, H Zhang, K Zhang, L Lin, W Zuo CVPRW 2018, 2018 | 683 | 2018 |
| Plug-and-Play Image Restoration with Deep Denoiser Prior K Zhang, Y Li, W Zuo, L Zhang, L Van Gool, R Timofte IEEE TPAMI, code: https://github.com/cszn/DPIR, 2021 | 492 | 2021 |
| Deep Unfolding Network for Image Super-Resolution K Zhang, L Van Gool, R Timofte CVPR 2020, code: https://github.com/cszn/USRNet, 2020 | 471 | 2020 |
| End-to-End Blind Image Quality Assessment Using Deep Neural Networks K Ma, W Liu, K Zhang, Z Duanmu, Z Wang, W Zuo IEEE TIP 2018, 2018 | 441 | 2018 |
| LocalViT: Bringing Locality to Vision Transformers Y Li, K Zhang, J Cao, R Timofte, L Van Gool arXiv preprint arXiv:2104.05707, 2021 | 370 | 2021 |
| Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels K Zhang, W Zuo, L Zhang CVPR 2019, code: https://github.com/cszn/DPSR, 2019 | 361 | 2019 |
| Ntire 2018 challenge on single image super-resolution: Methods and results R Timofte, S Gu, J Wu, L Van Gool CVPRW 2018, 2018 | 316 | 2018 |
| Designing a Practical Degradation Model for Deep Blind Image Super-Resolution K Zhang, J Liang, L Van Gool, R Timofte ICCV 2021; https://github.com/cszn/BSRGAN, 2021 | 313 | 2021 |
| Neural Blind Deconvolution Using Deep Priors D Ren, K Zhang, Q Wang, Q Hu, W Zuo CVPR 2020, code: https://github.com/csdwren/SelfDeblur, 2020 | 238 | 2020 |
| Extreme learning machine and adaptive sparse representation for image classification J Cao, K Zhang, M Luo, C Yin, X Lai Neural networks 81, 91-102, 2016 | 216 | 2016 |
| Outlier-robust extreme learning machine for regression problems K Zhang, M Luo Neurocomputing 151, 1519-1527, 2015 | 212 | 2015 |
| VRT: A Video Restoration Transformer J Liang, J Cao, Y Fan, K Zhang, R Ranjan, Y Li, R Timofte, L Van Gool arXiv preprint arXiv:2201.12288, 2022 | 123 | 2022 |
| Ntire 2019 challenge on real image super-resolution: Methods and results J Cai, S Gu, R Timofte, L Zhang CVPRW 2019, 2019 | 113 | 2019 |
Radu TimofteHumboldt Professor for AI and Computer Vision, University of Würzburg在 uni-wuerzburg.de 的电子邮件经过验证
