Hyperspectral Unmixing Powered by Deep Image Priors and Denoising Regularization

Min Zhao, Jie Chen

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Properly exploiting image properties is crucial for boosting the hyperspectral unmixing performance. Recent advanced image processing methods use deep architectures to learn image priors. However, these deep priors take effect in an implicit manner and it is nontrivial to characterize their properties. Introducing extra regularization terms is an explicit way of encoding image priors, and the plug-and-play technique enables to construct priors from data by denoisers. In this work, we propose a new unmixing framework to combine both the deep image priors (DIP) and plug-and-play (PnP) priors to further enhance the unmixing performance. The alter-nating direction method of multipliers (ADMM) framework is used to separate the optimization problem into two subproblems. The first one is solved using a U-net training step to obtain DIP, and a proximal denoising step is then used to solve the second subproblem to add denoiser priors. Experiment results demonstrate the effectiveness of our proposed method.

源语言英语
主期刊名IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
出版商Institute of Electrical and Electronics Engineers Inc.
1776-1779
页数4
ISBN(电子版)9781665427920
DOI
出版状态已出版 - 2022
活动2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, 马来西亚
期限: 17 7月 202222 7月 2022

出版系列

姓名International Geoscience and Remote Sensing Symposium (IGARSS)
2022-July

会议

会议2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
国家/地区马来西亚
Kuala Lumpur
时期17/07/2222/07/22

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