OPT OpenIR  > 空间光学应用研究室
A Deep Learning Approach to Real-Time Recovery for Compressive Hyper Spectral Imaging
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.
2017
Conference Name3rd IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)
Source Publication2017 IEEE 3RD INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC)
Pages1030-1034
Conference Date2017-10-03
Conference PlaceChongqing, PEOPLES R CHINA
Publication PlaceNEW YORK
PublisherIEEE
Contribution Rank1
Abstract

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.

KeywordCompressive Coded Hs Imaging Deep Learning Fully-connected Network Real-time
Subject AreaAutomation & Control Systems
Department空间光学应用研究室
DOI10.1109/ITOEC.2017.8122510
Indexed ByEI ; ISTP
ISBN978-1-5090-5363-6
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/29890
Collection空间光学应用研究室
Corresponding AuthorLi, RM (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China.
Affiliation1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
Recommended Citation
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]. NEW YORK:IEEE,2017:1030-1034.
Files in This Item:
File Name/Size DocType Version Access License
A Deep Learning Appr(935KB)会议论文 暂不开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Ruimin]'s Articles
[Zeng, Yang]'s Articles
[Wen, Desheng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Ruimin]'s Articles
[Zeng, Yang]'s Articles
[Wen, Desheng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Ruimin]'s Articles
[Zeng, Yang]'s Articles
[Wen, Desheng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.