四川师范大学学报（自然科学版）2025，Vol.48Issue(5)：637-660,24.DOI:10.3969/j.issn.1001-8395.2025.05.004
压缩感知的理论、算法及应用
Theory,Algorithms and Applications of Compressed Sensing
摘要
Abstract
Compressed sensing(CS)is an innovative framework for signal sampling,which breaks through the limitations of the Nyquist sampling theorem,significantly reducing the cost of data sampling,storage,and transmission.It has wide applications in fields such as image processing and wireless communication.This paper provides a detailed explanation of the theory,algorithms,and appli-cations of CS.For linear CS,the paper explores several representative CS model frameworks in depth,analyzes the advantages and lim-itations of each model framework,and summarizes their corresponding solution algorithms.In terms of nonlinear CS,this paper eluci-dates its related models,fundamental theory,and algorithms,particularly summarizing the theory and algorithms for one-bit CS and sparse phase retrieval.In addition,this paper explores the challenges and potential future research directions in the field of CS.The objective of this paper is to provide a comprehensive and in-depth reference resource for researchers and application engineers in CS,aiming to promote knowledge sharing and technological innovation,thereby fostering future developments and breakthroughs in both the-ory and application.关键词
稀疏优化/信号恢复/线性压缩感知/非线性压缩感知Key words
sparse optimization/signal recovery/linear compressed sensing/nonlinear compressed sensing分类
电子信息工程引用本文复制引用
温金明..压缩感知的理论、算法及应用[J].四川师范大学学报（自然科学版）,2025,48(5):637-660,24.基金项目
国家自然科学基金面上项目(12271215)、国家自然科学基金—天元基金访问学者项目(12326378)、密码科学技术国家重点实验室开放课题重点项目(MMKFKT202107) （12271215）
