| Understanding convolution for semantic segmentation P Wang, P Chen, Y Yuan, D Liu, Z Huang, X Hou, G Cottrell 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 1451-1460, 2018 | 2364 | 2018 |
| 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, ... Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE …, 2017 | 2078 | 2017 |
| Enlightengan: Deep light enhancement without paired supervision Y Jiang, X Gong, D Liu, Y Cheng, C Fang, X Shen, J Yang, P Zhou, ... IEEE Transactions on Image Processing 30, 2340-2349, 2021 | 1679 | 2021 |
| Deep networks for image super-resolution with sparse prior Z Wang, D Liu, J Yang, W Han, T Huang Proceedings of the IEEE International Conference on Computer Vision, 370-378, 2015 | 1016 | 2015 |
| Non-local recurrent network for image restoration D Liu, B Wen, Y Fan, CC Loy, TS Huang Advances in Neural Information Processing Systems 31, 1673-1682, 2018 | 712 | 2018 |
| Robust Single Image Super-Resolution via Deep Networks With Sparse Prior D Liu, Z Wang, B Wen, J Yang, W Han, TS Huang IEEE Transactions on Image Processing 25 (7), 3194-3207, 2016 | 301 | 2016 |
| Studying very low resolution recognition using deep networks Z Wang, S Chang, Y Yang, D Liu, TS Huang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 280 | 2016 |
| Robust video super-resolution with learned temporal dynamics D Liu, Z Wang, Y Fan, X Liu, Z Wang, S Chang, T Huang Computer Vision (ICCV), 2017 IEEE International Conference on, 2526-2534, 2017 | 278 | 2017 |
| Image super-resolution via dual-state recurrent networks W Han, S Chang, D Liu, M Yu, M Witbrock, TS Huang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 261 | 2018 |
| When image denoising meets high-level vision tasks: A deep learning approach D Liu, B Wen, X Liu, Z Wang, TS Huang International Joint Conference on Artificial Intelligence, 2018 | 242 | 2018 |
| D3: Deep dual-domain based fast restoration of JPEG-compressed images Z Wang, D Liu, S Chang, Q Ling, Y Yang, TS Huang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 240 | 2016 |
| Pyramid Attention Network for Image Restoration Y Mei, Y Fan, Y Zhang, J Yu, Y Zhou, D Liu, Y Fu, TS Huang, H Shi International Journal of Computer Vision 131 (12), 3207-3225, 2023 | 186 | 2023 |
| Learning progressive joint propagation for human motion prediction Y Cai, L Huang, Y Wang, TJ Cham, J Cai, J Yuan, J Liu, X Yang, Y Zhu, ... Proceedings of the European Conference on Computer Vision (ECCV), 2020 | 173 | 2020 |
| Residual Local Feature Network for Efficient Super-Resolution F Kong, M Li, S Liu, D Liu, J He, Y Bai, F Chen, L Fu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 171 | 2022 |
| Connecting image denoising and high-level vision tasks via deep learning D Liu, B Wen, J Jiao, X Liu, Z Wang, TS Huang IEEE Transactions on Image Processing 29, 3695-3706, 2020 | 166 | 2020 |
| Experiments on deep learning for speech denoising. D Liu, P Smaragdis, M Kim INTERSPEECH, 2685-2689, 2014 | 155 | 2014 |
| Image aesthetics assessment using Deep Chatterjee's machine Z Wang, D Liu, S Chang, F Dolcos, D Beck, T Huang 2017 International Joint Conference on Neural Networks (IJCNN), 941-948, 2017 | 140* | 2017 |
| Balanced two-stage residual networks for image super-resolution Y Fan, H Shi, J Yu, D Liu, W Han, H Yu, Z Wang, X Wang, TS Huang Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE …, 2017 | 129 | 2017 |
| Ntire 2022 challenge on efficient super-resolution: Methods and results Y Li, K Zhang, R Timofte, L Van Gool, F Kong, M Li, S Liu, Z Du, D Liu, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 123 | 2022 |
| Learning Temporal Dynamics for Video Super-Resolution: A Deep Learning Approach D Liu, Z Wang, Y Fan, X Liu, Z Wang, S Chang, X Wang, TS Huang IEEE Transactions on Image Processing 27 (7), 3432-3445, 2018 | 93 | 2018 |
Zhangyang (Atlas) WangXTX Markets & University of Texas at Austin在 utexas.edu 的电子邮件经过验证
