摘要
随着科技的发展,呈爆炸式增长的现代数据给数据的采集、传输和储存产生了巨大的压力和浪费.压缩感知理论(Compressed Sensing,CS)的提出有效缓解了以上压力.本文在介绍CS理论框架的基础上阐述其重构算法的分类和发展,着重介绍了稀疏重建模型和低秩重建模型对应的重建算法及各算法的特点.
With the development of science and technology, the explosive growth of modern data has brought tremendous pressure and waste in data acquisition, transmission and storage. Compressed Sensing(CS)has effectively relieved these pressures. This paper describes the classification and development of reconstruction algorithms based on the CS theoretical framework. Based on the sparse reconstruction model and the low rank matrix model corresponding,various reconstruction algorithms are introduced.
作者
刘蕾
李建东
闫敬文
Liu Lei;Li Jiandong;Yan Jingwen(Medical College, Shantou University, Shantou, 515063, Guangdong, China;College of Engineering, Shantou University, Shantou, 515063, Guangdong, China)
出处
《汕头大学学报（自然科学版）》
2019年第1期3-12,共10页
Journal of Shantou University：Natural Science Edition
基金
国家自然科学基金资助项目(61672335
61601276)
广东省自然科学基金资助项目(2016A030310077)
广东省创新强校项目(2017KTSCX121)
山东省高等学校科技计划项目(J16L158)
关键词
压缩感知
稀疏重建
低秩重建
compress sensing
sparse representation
low rank reconstruction
