| Active contour model based on local and global intensity information for medical image segmentation S Zhou, J Wang, S Zhang, Y Liang, Y Gong Neurocomputing 186, 107-118, 2016 | 169 | 2016 |
| Incorporating image priors with deep convolutional neural networks for image super-resolution Y Liang, J Wang, S Zhou, Y Gong, N Zheng Neurocomputing 194, 340-347, 2016 | 90 | 2016 |
| Discriminative feature learning with foreground attention for person re-identification S Zhou, J Wang, D Meng, Y Liang, Y Gong, N Zheng IEEE Transactions on Image Processing 28 (9), 4671-4684, 2019 | 70 | 2019 |
| Image quality assessment using similar scene as reference Y Liang, J Wang, X Wan, Y Gong, N Zheng Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 63 | 2016 |
| Single-Image super-resolution-When model adaptation matters Y Liang, R Timofte, J Wang, S Zhou, Y Gong, N Zheng Pattern Recognition 116, 2021 | 35* | 2021 |
| Single image super-resolution with a parameter economic residual-like convolutional neural network Z Yang, K Zhang, Y Liang, J Wang MultiMedia Modeling: 23rd International Conference, MMM 2017, Reykjavik …, 2017 | 18 | 2017 |
| Multi-attribute group decision making based on multigranulation probabilistic models with interval-valued neutrosophic information C Zhang, D Li, X Kang, Y Liang, S Broumi, AK Sangaiah Mathematics 8 (2), 223, 2020 | 16 | 2020 |
| Self-supervised Learning and Adaptation for Single Image Dehazing. Y Liang, B Wang, W Zuo, J Liu, W Ren IJCAI, 1137-1143, 2022 | 15 | 2022 |
| Single image super-resolution via a lightweight residual convolutional neural network Y Liang, Z Yang, K Zhang, Y He, J Wang, N Zheng arXiv preprint arXiv:1703.08173, 2017 | 13 | 2017 |
| Microstructure evolution of lithium-ion battery electrodes at different states of charge: Deep learning-based segmentation Y Yang, N Li, B Wang, N Li, K Gao, Y Liang, Y Wei, L Yang, WL Song, ... Electrochemistry Communications 136, 107224, 2022 | 11 | 2022 |
| Incorporating image degeneration modeling with multitask learning for image super-resolution Y Liang, J Wang, S Zhang, Y Gong 2015 IEEE International Conference on Image Processing (ICIP), 2110-2114, 2015 | 7 | 2015 |
| Bin; Wang, L X Li, D Shi, Q Su, H Tong Bin, 0 | 6 | |
| Learning visual co-occurrence with auto-encoder for image super-resolution Y Liang, J Wang, S Zhang, Y Gong Signal and Information Processing Association Annual Summit and Conference …, 2014 | 5 | 2014 |
| Learning to describe color composition of visual objects Y Liu, Y Liang, Z Yuan, N Zheng Proceedings of the 21st International Conference on Pattern Recognition …, 2012 | 4 | 2012 |
| Progressive residual learning for single image dehazing Y Liang, B Wang, J Liu, D Li, Y Qian, W Ren arXiv preprint arXiv:2103.07973, 2021 | 3 | 2021 |
| Progressive depth learning for single image dehazing Y Liang, B Wang, J Liu, D Li, S Zhou, W Ren arXiv preprint arXiv:2102.10514, 2021 | 3 | 2021 |
| Learning hierarchical dynamics with spatial adjacency for image enhancement Y Liang, B Wang, W Ren, J Liu, W Wang, W Zuo Proceedings of the 30th ACM International Conference on Multimedia, 2767-2776, 2022 | 2 | 2022 |
| Combining Enhancement and Fusion for Low-Light Visible and Infrared Image S Li, K Zhang, Y Liang, C Zhang, X Shangguan 2023 China Automation Congress (CAC), 7730-7735, 2023 | | 2023 |
| Advances in Image Enhancement C Tian, W Ren, Y Liang MDPI-Multidisciplinary Digital Publishing Institute, 2023 | | 2023 |
| Learning Qualitative and Quantitative Image Quality Assessment Y Liang, J Wang, Z Yang, Y Gong, N Zheng Advances in Multimedia Information Processing-PCM 2016: 17th Pacific-Rim …, 2016 | | 2016 |
