.png){kind=icon}
返回
A nonconvex penalty function with integral convolution approximation for compressed sensing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2019.01.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a novel nonconvex penalty function for compressed sensing using integral convolution approximation. It is well known that an unconstrained optimization criterion based on l(1)-norm easily underestimates the large component in signal recovery. Moreover, most methods either perform well only under the Gaussian random measurement matrix satisfying restricted isometry property or the highly coherent measurement matrix, which both can not be established at the same time. We introduce a new solver to address both of these concerns by adopting a frame of the difference between two convex functions with integral convolution approximation. What's more, to better boost the recovery performance, a weighted version of it is also provided. Experimental results suggest the effectiveness and robustness of our methods through several signal reconstruction examples in term of success rate and signal-to-noise ratio. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Compressed sensing
Penalty function
Integral convolution
Restricted isometry property
Coherence
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
Restricted p-isometry properties of nonconvex block-sparse compressed sensing非凸块稀疏压缩感知的受限p-等距特性.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏分享收藏
Mobile markerless augmented reality and its application in forensic medicine移动无标记增强现实技术及其在法医学中的应用
PREAI
分享收藏
查看更多内容