| Hyperspectral Image Restoration via Global L1-2 Spatial–Spectral Total Variation Regularized Local Low-Rank Tensor Recovery H Zeng, X Xie, H Cui, H Yin, J Ning IEEE Transactions on Geoscience and Remote Sensing 59 (4), 3309-3325, 2020 | 55 | 2020 |
| Hyperspectral image denoising via global spatial-spectral total variation regularized nonconvex local low-rank tensor approximation H Zeng, X Xie, J Ning Signal Processing 178, 107805, 2021 | 40 | 2021 |
| Hyperspectral image restoration via cnn denoiser prior regularized low-rank tensor recovery H Zeng, X Xie, H Cui, Y Zhao, J Ning CVIU 2020, code: https://github.com/NavyZeng/DPLRTA 197, 103004, 2020 | 31 | 2020 |
| Hyperspectral image denoising via combined non-local self-similarity and local low-rank regularization H Zeng, X Xie, W Kong, S Cui, J Ning IEEE Access 8, 50190-50208, 2020 | 22 | 2020 |
| Multimodal Core Tensor Factorization and its Applications to Low-Rank Tensor Completion H Zeng, J Xue, HQ Luong, W Philips IEEE Transactions on Multimedia, 2022 | 19 | 2022 |
| Enhanced nonconvex low-rank approximation of tensor multi-modes for tensor completion H Zeng, Y Chen, X Xie, J Ning IEEE TCI, code: https://github.com/NavyZeng/LRATM 7, 164-177, 2021 | 18 | 2021 |
| Asymmetry total variation and framelet regularized nonconvex low-rank tensor completion Y Chen, T Xu, X Zhao, H Zeng, Y Xu, J Chen Signal Processing 206, 108901, 2023 | 12 | 2023 |
| Mipi 2022 challenge on quad-bayer re-mosaic: Dataset and report Q Yang, G Yang, J Jiang, C Li, R Feng, S Zhou, W Sun, Q Zhu, CC Loy, ... European Conference on Computer Vision, 21-35, 2022 | 8 | 2022 |
| Generalized Nonconvex Low-rank Tensor Representation for Hyperspectral Anomaly Detection H Qin, Q Shen, H Zeng, Y Chen, G Lu IEEE TGRS, code: https://github.com/Qinhao06/GNLTR, 2023 | 7 | 2023 |
| Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising H Zeng, J Cao, K Feng, S Huang, H Zhang, H Luong, W Philips arXiv preprint arXiv:2305.04047, 2023 | 5 | 2023 |
| L1-2 spectral-spatial total variation regularized hyperspectral image denosing (L1-2 空谱全变差正则化下的高光谱图像去噪) HJ Zeng, JW Jiang, JJ Zhao Acta Photonica Sinica 48 (10), 214-228, 2019 | 5 | 2019 |
| From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images S Huang, H Zhang, H Zeng, A Pižurica Remote Sensing 15 (11), 2832, 2023 | 4 | 2023 |
| Tensor Completion Using Bilayer Multimode Low-Rank Prior and Total Variation H Zeng, S Huang, Y Chen, S Liu, HQ Luong, W Philips IEEE Transactions on Neural Networks and Learning Systems, 2023 | 4 | 2023 |
| All of Low-rank and Sparse: A Recast Total Variation Approach to Hyperspectral Denoising H Zeng, S Huang, Y Chen, H Luong, W Philips IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 | 3 | 2023 |
| 基于频率加权张量核范数的高光谱图像复原 刘盛， 曾海金， 孔文凤， 张鹏丹 中国图象图形学报, 2021 | 3 | 2021 |
| MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing H Zeng, K Feng, S Huang, J Cao, Y Chen, H Zhang, H Luong, W Philips arXiv preprint arXiv:2303.13404, 2023 | 2 | 2023 |
| Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising H Zeng, S Huang, Y Chen, H Luong, W Philips arXiv preprint arXiv:2204.12879, 2022 | 2 | 2022 |
| Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor H Zeng, K Feng, J Cao, S Huang, Y Zhao, H Luong, J Aelterman, ... International Journal of Computer Vision (IJCV), 2024 | 1 | 2024 |
| Learnable Spatial-Spectral Transform-Based Tensor Nuclear Norm for Multi-Dimensional Visual Data Recovery S Liu, J Leng, XL Zhao, H Zeng, Y Wang, JH Yang IEEE Transactions on Circuits and Systems for Video Technology, 2023 | 1 | 2023 |
| Hyperspectral Image Restoration via Global Total Variation Regularized Local Nonconvex Low-Rank Matrix Approximation H Zeng, X Xie, J Ning IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium …, 2020 | 1 | 2020 |
Jifeng NingCollege of Information Engineering, Northwest A&F University在 nwsuaf.edu.cn 的电子邮件经过验证
