[1]赵春晖,刘巍.压缩感知理论及其在成像技术中的应用[J].智能系统学报,2012,7(01):21-32.
 ZHAO Chunhui,LIU Wei.Compressive sensing theory and its application in imaging technology[J].CAAI Transactions on Intelligent Systems,2012,7(01):21-32.
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压缩感知理论及其在成像技术中的应用(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第7卷
期数:
2012年01期
页码:
21-32
栏目:
学术论文—智能系统
出版日期:
2012-02-25

文章信息/Info

Title:
Compressive sensing theory and its application in imaging technology
文章编号:
1673-4785(2012)01-0021-12
作者:
赵春晖刘巍
哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001
Author(s):
ZHAO Chunhui LIU Wei
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
压缩感知压缩成像稀疏分解观测矩阵重建算法
Keywords:
compressive sensing (CS) compressive imaging (CI) sparse decomposition measurement matrix reconstruction algorithm
分类号:
TP391.41;TN911.73
文献标志码:
A
摘要:
在传统的Shannon/Nyquist采样定理指导下,信号处理往往面临两大难题:A/D转换器技术的限制和海量采样数据的处理压力.压缩感知(CS)理论表明当信号具有稀疏性或可压缩性时,可以通过全局非自适应线性投影的方式,用远低于Shannon/Nyquist采样定理要求的频率获取信号的全部信息.以CS理论为基础的压缩成像(CI)技术在近年来得到了快速的发展.在概述CS理论的基础上,着重介绍了CI技术的原理及其发展状况,并从稀疏分解、观测矩阵的构造和重建算法3个方面对其关键问题进行了分析.CI系统可以显著节省感光器件的数量,避免了传统成像技术“先采样再压缩”方式带来的资源浪费.
Abstract:
Under the guidance of the traditional Shannon/Nyquist sampling theorem, signal processing often faces two problems: technology limitation of the A/D converter and processing pressure caused by a mass of sampled data. Compressive sensing (CS) theory suggests that when the signal is sparse or compressible, by means of global nonadaptive linear projection, all the signal information can be obtained with the samples much less than the sampling theorem required. CS theory based compressive imaging (CI) technology has been developed significantly in recent years. This paper first reviewed the principles of CS, and on this basis, discussed the theory and development of CI technology. The key issues of CI were also analyzed from three aspects of sparse decomposition, construction of measurement matrix, and the reconstruction algorithm. The CI system can significantly cut down on the number of photosensitive devices to avoid resource waste caused by a traditional “samplethencompress” framework. 

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备注/Memo

备注/Memo:
收稿日期: 2011-10-28.
网络出版时间:2012-02-16.
基金项目:国家自然科学基金资助项目(61077079);高等学校博士学科点专项基金资助项目(20102304110013);哈尔滨市优秀学科带头人基金资助项目(2009RFXXG034).
通信作者:赵春晖.         E-mail:zhaochunhui@hrbeu.edu.cn.
作者简介:
赵春晖,男,1965 年生,教授,博士生导师,全国优秀教师,国家级教学名师.IEEE会员,中国电子学会和中国通信学会高级会员,中国航空学会和中国图象图形学会会员.主要研究方向为智能信息与图像处理、非线性信号处理和通信信号处理.发表学术论文300 余篇,被SCI、EI、ISTP 检索160余篇,出版著作和教材10 部.
刘巍,男,1982年生,博士研究生,主要研究方向为非线性信号与图像处理.
更新日期/Last Update: 2012-05-07