| ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks X Wang, K Yu, S Wu, J Gu, Y Liu, C Dong, Y Qiao, C Change Loy European Conference on Computer Vision (ECCV) Workshop, 2018, 63-79, 2018 | 4364 | 2018 |
| Interpreting the Latent Space of GANs for Semantic Face Editing Y Shen, J Gu, X Tang, B Zhou Computer Vision and Pattern Recognition (CVPR), 2020, 9243-9252, 2020 | 1123 | 2020 |
| Blind Super-Resolution With Iterative Kernel Correction J Gu, H Lu, W Zuo, C Dong Computer Vision and Pattern Recognition (CVPR), 2019, 1604-1613, 2019 | 556 | 2019 |
| Image Processing Using Multi-Code GAN Prior J Gu, Y Shen, B Zhou Computer Vision and Pattern Recognition (CVPR), 2020, 3012-3021, 2020 | 350 | 2020 |
| Interpreting Super-Resolution Networks with Local Attribution Maps J Gu, C Dong Computer Vision and Pattern Recognition (CVPR), 2021, 9199-9208, 2021 | 186 | 2021 |
| Suppressing Model Overfitting for Image Super-Resolution Networks R Feng, J Gu, Y Qiao, C Dong Computer Vision and Pattern Recognition (CVPR) Workshop, 2019, 1964-1973, 2019 | 169* | 2019 |
| Pipal: a large-scale image quality assessment dataset for perceptual image restoration J Gu, H Cai, H Chen, X Ye, J Ren, C Dong European Conference on Computer Vision (ECCV), 2020, 633-651, 2020 | 159* | 2020 |
| Blind image super-resolution: A survey and beyond A Liu, Y Liu, J Gu, Y Qiao, C Dong IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022 | 154 | 2022 |
| Blueprint Separable Residual Network for Efficient Image Super-Resolution Z Li, Y Liu, X Chen, H Cai, J Gu, Y Qiao, C Dong Computer Vision and Pattern Recognition (CVPR) Workshop, 2022, 2022 | 136 | 2022 |
| Dual Aggregation Transformer for Image Super-Resolution Z Chen, Y Zhang, J Gu, L Kong, X Yang, F Yu International Conference on Computer Vision (ICCV), 2023, 2023 | 99 | 2023 |
| NTIRE 2022 challenge on perceptual image quality assessment J Gu, H Cai, C Dong, JS Ren, R Timofte Computer Vision and Pattern Recognition (CVPR) Workshop, 2022, 951-967, 2022 | 98 | 2022 |
| Attention in attention network for image super-resolution H Chen, J Gu, Z Zhang arXiv preprint arXiv:2104.09497, 2021 | 89 | 2021 |
| Cross Aggregation Transformer for Image Restoration C Zheng, Y Zhang, J Gu, Y Zhang, L Kong, X Yuan Neural Information Processing Systems (NeurIPS), 2022, 2022 | 86 | 2022 |
| Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline G Qian, Y Wang, C Dong, JS Ren, W Heidrich, B Ghanem, J Gu International Conference on Computational Photography (ICCP), 2022, 2022 | 82* | 2022 |
| Accurate image restoration with attention retractable transformer J Zhang, Y Zhang, J Gu, Y Zhang, L Kong, X Yuan International Conference on Learning Representations (ICLR), 2023, 2023 | 75 | 2023 |
| Reflash Dropout in Image Super-Resolution X Kong, X Liu, J Gu, Y Qiao, C Dong Computer Vision and Pattern Recognition (CVPR), 2022, 2022 | 71 | 2022 |
| Efficient Image Super-Resolution using Vast-Receptive-Field Attention L Zhou, H Cai, J Gu, Z Li, Y Liu, X Chen, Y Qiao, C Dong European Conference on Computer Vision (ECCV) Workshop, 2022, 2022 | 56 | 2022 |
| Rethinking Alignment in Video Super-Resolution Transformers S Shi, J Gu, L Xie, X Wang, Y Yang, C Dong Neural Information Processing Systems (NeurIPS), 2022, 2022 | 49 | 2022 |
| Image quality assessment for perceptual image restoration: A new dataset, benchmark and metric J Gu, H Cai, H Chen, X Ye, J Ren, C Dong arXiv preprint arXiv:2011.15002, 2020 | 42 | 2020 |
| Super Resolution Perception for Smart Meter Data G Liu, J Gu, J Zhao, F Wen, G Liang Information Sciences 526, 263-273, 2020 | 42 | 2020 |
Chao DongShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences在 siat.ac.cn 的电子邮件经过验证
