基于压缩感知的图像稀疏表示和重构  

Sparse representation and reconstruction of image based on compressed sensing

作　　者：许婷婷[1] 洪丽华[2] 刘真祥[1] 张静敏 周卫红[1,3] XU Ting-ting;HONG Li-hua;LIU Zhen-xiang;ZHANG Jing-min;ZHOU Wei-hong(School of Mathematics and Computer Science,Yunnan Minzu University,Kunming 650500,China;Software Engineering Department,Xiamen Institute of Software Technology,Xiamen 361000,China;Key Laboratory of the Structure and Evolution of Celestial Objects,Chinese Academy of Sciences,Kunming 650011,China)

机构地区：[1]云南民族大学数学与计算机科学学院,云南昆明650500 [2]厦门软件职业技术学院软件工程系,福建厦门361000 [3]中国科学院天体结构与演化重点实验室,云南昆明650011

出　　处：《云南民族大学学报（自然科学版）》2018年第2期147-153,共7页Journal of Yunnan Minzu University:Natural Sciences Edition

基　　金：国家自然科学基金(61561053);中国科学院天体结构与演化重点实验室(OP201512)

摘　　要：在信号可稀疏表示的基础上,压缩感知理论将数据的采集和压缩集于一身,从较少的观测值中重构出原始信号,突破了以奈奎斯特采样定理为基础的传统采样方式的局限性,降低了对信号采样率的要求.首先介绍了压缩感知的基本理论和各类重构算法,并在时间复杂度和重构精度上对算法作出分析比较,然后基于压缩感知理论综述图像稀疏表示和重构算法的研究进展及其相关方面的应用,最后对压缩感知在稀疏表示和重构方面作出了总结和展望.The compressed sensing theory can integrate data acquisition with data compression based on signal sparse representation,and reconstruct the original signal from less observed value,which is a breakthrough in the limits of the traditional sampling based on the Nyquist sampling theory and reduces the requirements of the sampling rate.Firstly,this paper expounds the basic theoretical framework of compressed sensing and all kinds of reconstruction algorithms,and analyzes its advantages and disadvantages in time complexity and reconstruction precision.Secondly,the research progress of sparse representation and reconstruction algorithms of images based on the compressed sensing theory and the relevant application are reviewed.Finally,it gives a summary and prospect of the research on sparse representation and reconstruction based on compressed sensing.

关 键 词：压缩感知 稀疏表示 信号处理 重构算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]