Recursive Recovery of Sparse Signal Sequences From Compressive Measurements: A Review

被引:64
|
作者
Vaswani, Namrata [1 ]
Zhan, Jinchun [1 ]
机构
[1] Iowa State Univ, Dept Elect & Comp Engn, Ames, IA 50011 USA
基金
美国国家科学基金会;
关键词
Compressed sensing; sparse recovery; recursive reconstruction; compressive measurements; UNCERTAINTY PRINCIPLES; RECONSTRUCTION; ALGORITHM; SYSTEMS; REPRESENTATIONS; APPROXIMATION; EQUATIONS; SUPPORT; BOUNDS; LMS;
D O I
10.1109/TSP.2016.2539138
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this overview article, we review the literature on design and analysis of recursive algorithms for reconstructing a time sequence of sparse signals from compressive measurements. The signals are assumed to be sparse in some transform domain or in some dictionary. Their sparsity patterns can change with time, although, in many practical applications, the changes are gradual. An important class of applications where this problem occurs is dynamic projection imaging, e.g., dynamic magnetic resonance imaging (MRI) for real-time medical applications such as interventional radiology, or dynamic computed tomography.
引用
收藏
页码:3523 / 3549
页数:27
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