HYPERSPECTRAL IMAGE SUPER-RESOLUTION WITH DEEP PRIORS AND DEGRADATION MODEL INVERSION

Xiuheng Wang, Jie Chen, Cédric Richard

科研成果: 书/报告/会议事项章节会议稿件同行评审

13 引用 (Scopus)

摘要

To overcome inherent hardware limitations of hyperspectral imaging systems with respect to their spatial resolution, fusion-based hyperspectral image (HSI) super-resolution is attracting increasing attention. This technique aims to fuse a low-resolution (LR) HSI and a conventional high-resolution (HR) RGB image in order to obtain an HR HSI. Recently, deep learning architectures have been used to address the HSI super-resolution problem and have achieved remarkable performance. However, they ignore the degradation model even though this model has a clear physical interpretation and may contribute to improving the performance. We address this problem by proposing a method that, on the one hand, makes use of the linear degradation model in the data-fidelity term of the objective function and, on the other hand, utilizes the output of a convolutional neural network for designing a deep prior regularizer in spectral and spatial gradient domains. Experiments show the performance improvement achieved with this strategy.

源语言英语
主期刊名2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
2814-2818
页数5
ISBN(电子版)9781665405409
DOI
出版状态已出版 - 2022
活动2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 - Hybrid, 新加坡
期限: 22 5月 202227 5月 2022

出版系列

姓名ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2022-May
ISSN(印刷版)1520-6149

会议

会议2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022
国家/地区新加坡
Hybrid
时期22/05/2227/05/22

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