Compressive hyperspectral imaging mask optimization
Authors: 
Binbin Lv, Jiamin Wu, Chenggang Yan, Xinhong Hao, Guiguang Ding, Yongdong Zhang, Qionghai DaiAuthors Info & Claims
Binbin Lv, Jiamin Wu, Chenggang Yan, Xinhong Hao, Guiguang Ding, Yongdong Zhang, Qionghai DaiAuthors Info & ClaimsArticle No.: 2, Pages 1 - 5
Abstract
Hyperspectral imaging is a hot topic nowadays. It is an urgent problem to be solved how to achieve swift hyperspectral imaging. In this thesis, our primary purpose is to further optimize how to place a mask in front of a sensor in order to achieve compressed Hyperspectral imaging. We apply optimized projection matrix, matrix differential, projection analysis and other related knowledge to optimizing this realistic matter. After we simply introduce the background of hyperspectral imaging, we will firstly present the basic principle of compressed hyperspectral imaging based on mask, and then mainly analyze the way to achieve projection matrix optimizing algorithm and the challenges these sort of realistic problems face. Finally, we compare the experiment results of these two methods, and the rebuilding results before and after the optimizing.
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Index Terms
- Compressive hyperspectral imaging mask optimization
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Published In
August 2018
243 pages
ISBN:9781450365208
DOI:10.1145/3240876
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Association for Computing Machinery
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Published: 17 August 2018
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ICIMCS'18
ICIMCS'18: The 10th International Conference on Internet Multimedia Computing and Service
August 17 - 19, 2018
Nanjing, China
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ICIMCS '18 Paper Acceptance Rate 46 of 116 submissions, 40%;
Overall Acceptance Rate 163 of 456 submissions, 36%
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