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 | |
会议名称 | 3rd IEEE Information Technology and Mechatronics Engineering Conference (ITOEC) |
会议录名称 | 2017 IEEE 3RD INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC) |
页码 | 1030-1034 |
会议日期 | 2017-10-03 |
会议地点 | Chongqing, PEOPLES R CHINA |
出版地 | NEW YORK |
出版者 | IEEE |
产权排序 | 1 |
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. |
关键词 | Compressive Coded Hs Imaging Deep Learning Fully-connected Network Real-time |
学科领域 | Automation & Control Systems |
作者部门 | 空间光学应用研究室 |
DOI | 10.1109/ITOEC.2017.8122510 |
收录类别 | EI ; ISTP |
ISBN号 | 978-1-5090-5363-6 |
语种 | 英语 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/29890 |
专题 | 空间光学技术研究室 |
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]. NEW YORK:IEEE,2017:1030-1034. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
A Deep Learning Appr(935KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论