| Deep learning for handling kernel/model uncertainty in image deconvolution Y Nan, H Ji Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 49 | 2020 |
| Variational-EM-based deep learning for noise-blind image deblurring Y Nan, Y Quan, H Ji Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 48 | 2020 |
| Nonblind image deblurring via deep learning in complex field Y Quan, P Lin, Y Xu, Y Nan, H Ji IEEE Transactions on Neural Networks and Learning Systems 33 (10), 5387-5400, 2021 | 26 | 2021 |
| Deep learning with adaptive hyper-parameters for low-dose CT image reconstruction Q Ding, Y Nan, H Gao, H Ji IEEE Transactions on Computational Imaging 7, 648-660, 2021 | 20 | 2021 |
| Un-supervised learning for blind image deconvolution via monte-carlo sampling J Li, Y Nan, H Ji Inverse Problems 38 (3), 035012, 2022 | 13 | 2022 |
| Self-supervised blind motion deblurring with deep expectation maximization J Li, W Wang, Y Nan, H Ji Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 4 | 2023 |
| INVERSE PROBLEMS NEWSLETTER J Li, Y Nan, H Ji, P Massa, F Benvenuto Inverse Problems 15, 343-344, 1999 | | 1999 |
