压缩感知理论与光学压缩成像系统  

Compressive sensing theory and optical compressive imaging systems

作　　者：严奉霞[1] 王泽龙[1] 朱炬波[1] 刘吉英[1] 

机构地区：[1]国防科技大学理学院,湖南长沙410073

出　　处：《国防科技大学学报》2014年第2期140-147,共8页Journal of National University of Defense Technology

基　　金：国家自然科学基金资助项目(61002024);国家部委资助项目

摘　　要：压缩感知理论为提升信息获取能力提供了新的思路,它表明当被探测信号具有稀疏性时,则获取信号所必需的测量数据与其稀疏度K量级相当,而远小于信号的维数N(Shannon采样定理所要求的采样数)。基于压缩感知理论的成像技术(压缩成像)则将感知、压缩和数据处理三个过程完美地结合在一起,避免了传统成像系统"先采样再压缩"方式带来的传感器和计算资源浪费。本文从稀疏性、投影测量矩阵的设计与可重构条件、压缩感知重构算法三个方面概述了压缩感知理论及进展,并以光学成像为背景,详细阐述了最近提出的几类光学压缩成像系统,最后,探讨了压缩感知及压缩成像方面目前所面临的一些挑战性问题。Compressive sensing provides a new way for increasing the ability of information acquisition. Compressive sensing asserts that it is possible to accurately reconstruct signals from sub-Nyquist sampling,provided some additional assumptions（ sparse or compressible） are made about the signal in question. The compressive imaging technology,which is based on the compressive sensing theory,integrates the processes of sensing,compression and processing perfectly,avoiding the resource waste caused by a traditional ＂sample-then-compress＂framework. With a review of some of the recent progress in compressive sensing theory from the following three aspects： sparsity,the design of measuring matrix and recovery conditions,the reconstruction algorithms,several optical compressive imaging systems are introduced,and some key challenges in this area have been discussed in the end.

关 键 词：压缩感知 光学压缩成像 稀疏表示 投影测量矩阵 

分 类 号：TP316[自动化与计算机技术—计算机软件与理论]