中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A Deep Learning Approach to Real-Time Recovery for Compressive Hyper Spectral Imaging

文献类型:会议论文

作者Li, Ruimin1,2; Zeng, Yang1,2; Wen, Desheng1; Song, Zongxi1; Li, RM (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China.
出版日期2017
会议日期2017-10-03
会议地点Chongqing, PEOPLES R CHINA
关键词Compressive Coded Hs Imaging Deep Learning Fully-connected Network Real-time
DOI10.1109/ITOEC.2017.8122510
页码1030-1034
英文摘要

Compressive coded hyper spectral (HS) imaging actualizes compressed sampling and snapshot acquisition of HS data, whereas current recovery algorithms take too long time to make real-time HS imaging satisfactory. This paper proposes a deep learning approach for compressive HS imaging to shorten the recovery time. A fully-connected network is designed to train a block-based non-linear reconstruction operator. There is a mergence after obtaining the recovery 3D blocks, followed with a block edge mean filter. The contribution of this approach is that it uses deep neural network to do the reconstruction of the HS data for the first time and it has low-complexity and needs less memory because of operating on local patches. The proposed method was validated on a public available HS dataset and the experimental results show that this approach is superior to the state-of-the-art in the recovery accuracy, and dramatically improves the reconstruction speed by 400 similar to 760 times.

产权排序1
会议录2017 IEEE 3RD INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC)
会议录出版者IEEE
学科主题Automation & Control Systems
会议录出版地NEW YORK
语种英语
ISBN号978-1-5090-5363-6
源URL[http://ir.opt.ac.cn/handle/181661/29890]  
专题西安光学精密机械研究所_空间光学应用研究室
通讯作者Li, RM (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China.
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714
Li, Ruimin,Zeng, Yang,Wen, Desheng,et al. A Deep Learning Approach to Real-Time Recovery for Compressive Hyper Spectral Imaging[C]. 见:. Chongqing, PEOPLES R CHINA. 2017-10-03.

入库方式: OAI收割

来源:西安光学精密机械研究所

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