| Dense Nested Attention Network for Infrared Small Target Detection B Li, C Xiao, L Wang, Y Wang, Z Lin, M Li, W An, Y Guo IEEE Transactions on Image Processing (TIP), 2021 | 460 | 2021 |
| Unsupervised Degradation Representation Learning for Blind Super-Resolution L Wang, Y Wang, X Dong, Q Xu, J Yang, W An, Y Guo CVPR 2021, 2021 | 408 | 2021 |
| Learning parallax attention for stereo image super-resolution L Wang, Y Wang, Z Liang, Z Lin, J Yang, W An, Y Guo CVPR 2019, 12250-12259, 2019 | 315 | 2019 |
| Exploring Sparsity in Image Super-Resolution for Efficient Inference L Wang, X Dong, Y Wang, X Ying, Z Lin, W An, Y Guo CVPR 2021, 2020 | 303 | 2020 |
| Disentangling light fields for super-resolution and disparity estimation Y Wang, L Wang, G Wu, J Yang, W An, J Yu, Y Guo IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022 | 238 | 2022 |
| Spatial-Angular Interaction for Light Field Image Super-Resolution Y Wang, L Wang, J Yang, W An, J Yu, Y Guo ECCV 2020, 2019 | 210 | 2019 |
| Deep Video Super-Resolution using HR Optical Flow Estimation L Wang, Y Guo, L Liu, Z Lin, X Deng, W An IEEE Transactions on Image Processing (TIP) 29, 4323--4336, 2020 | 181 | 2020 |
| Deformable 3D Convolution for Video Super-Resolution X Ying, L Wang, Y Wang, W Sheng, W An, Y Guo IEEE Signal Processing Letters, 2020 | 155 | 2020 |
| Light field image super-resolution using deformable convolution Y Wang, J Yang, L Wang, X Ying, T Wu, W An, Y Guo IEEE Transactions on Image Processing (TIP) 30, 1057-1071, 2020 | 149 | 2020 |
| Learning A Single Network for Scale-Arbitrary Super-Resolution L Wang, Y Wang, Z Lin, J Yang, W An, Y Guo ICCV 2021, 2021 | 146 | 2021 |
| Flickr1024: A large-scale dataset for stereo image super-resolution Y Wang, L Wang, J Yang, W An, Y Guo ICCVW 2019, 0-0, 2019 | 141 | 2019 |
| Parallax Attention for Unsupervised Stereo Correspondence Learning L Wang, Y Guo, Y Wang, Z Liang, Z Lin, J Yang, W An IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020 | 138 | 2020 |
| Light field image super-resolution with transformers Z Liang, Y Wang, L Wang, J Yang, S Zhou IEEE Signal Processing Letters 29, 563-567, 2022 | 130 | 2022 |
| A stereo attention module for stereo image super-resolution X Ying, Y Wang, L Wang, W Sheng, W An, Y Guo IEEE Signal Processing Letters 27, 496-500, 2020 | 130 | 2020 |
| Learning for video super-resolution through HR optical flow estimation L Wang, Y Guo, Z Lin, X Deng, W An ACCV 2018, 514-529, 2018 | 125 | 2018 |
| Occlusion-Aware Cost Constructor for Light Field Depth Estimation Y Wang, L Wang, Z Liang, J Yang, W An, Y Guo CVPR 2022, 19809-19818, 2022 | 102 | 2022 |
| Symmetric parallax attention for stereo image super-resolution Y Wang, X Ying, L Wang, J Yang, W An, Y Guo CVPRW 2021, 766-775, 2021 | 98 | 2021 |
| DeOccNet: Learning to See Through Foreground Occlusions in Light Fields Y Wang, T Wu, J Yang, L Wang, W An, Y Guo WACV 2020, 118-127, 2020 | 84 | 2020 |
| NTIRE 2023 challenge on stereo image super-resolution: Methods and results L Wang, Y Guo, Y Wang, J Li, S Gu, R Timofte, M Cheng, H Ma, Q Ma, ... CVPRW 2023, 1346-1372, 2023 | 81 | 2023 |
| Remote Sensing Image Super-Resolution Using Second-Order Multi-Scale Networks X Dong, L Wang, X Sun, X Jia, L Gao, B Zhang IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2020 | 71 | 2020 |
Yulan GuoProfessor, Sun Yat-sen UniversityE-mail megerősítve itt: sysu.edu.cn