摘要
压缩感知(compressed sensing,CS)是一种全新的信息采集与处理理论,它表明稀疏信号能够在远低于Shannon-Nyquist采样率的条件下被精确重构.现从压缩感知理论出发,对块稀疏信号重构算法进行研究,通过混合l2/lq(0 <q≤1)极小化方法,利用块-限制等距性质建立一类改进的精确恢复条件(无噪声情形),并给出有噪声情形下的误差分析结果.
Compressed sensing(CS) is a newly developed theoretical framework for information acquisition and processing,which shows that sparse signals can be recovered exactly from far less samples than those required by the classical Shannon-Nyquist theorem.The block-sparse signal recovery algorithm under the compressed sensing framework was mainly studied,and a class of improved exact recovery conditions based on the block restricted isometry property(RIP) were established in the noiseless cases via the mixed l2/lq(0<q≤1)norm minimization.Furthermore,the error analysis results were given in the noisy cases.
作者
周珺
黄尉
ZHOU Jun;HUANG Wei(School of Mathematics,Hefei University of Technology, Hefei 230009,P.R.China)
出处
《应用数学和力学》
CSCD
北大核心
2019年第2期167-180,共14页
Applied Mathematics and Mechanics
基金
国家自然科学基金重大研究计划(91538112)
国家自然科学基金青年科学基金(11201450)~~
