基于共轭梯度法的感知矩阵优化方法

TP399; 压缩感知理论中降低信号维数的关键问题是构造有效的测量矩阵.在已知稀疏基的情况下,基于ETF(Equiangular Tight Frame)框架的测量矩阵构造方法和稀疏信号重构过程均依赖于感知矩阵.为此,设计了一种基于共轭梯度法的感知矩阵优化方法,该方法简单易行,且所求结果的Gram矩阵与目标Gram矩阵更接近.实验结果表明,此感知矩阵优化方法在理论分析、实际图像应用及算法有效性上均具优势....

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Published in浙江大学学报（理学版） Vol. 46; no. 1; pp. 15 - 21
Main Authors 李昕艺, 刘三阳, 谢维
Format Journal Article
LanguageChinese
Published 西安电子科技大学 数学与统计学院,陕西 西安,710126 2019
Subjects
感知矩阵优化
共轭梯度法
压缩感知
ETF框架
Online AccessGet full text
ISSN1008-9497
DOI10.3785/j.issn.1008-9497.2019.01.003

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Abstract TP399; 压缩感知理论中降低信号维数的关键问题是构造有效的测量矩阵.在已知稀疏基的情况下,基于ETF(Equiangular Tight Frame)框架的测量矩阵构造方法和稀疏信号重构过程均依赖于感知矩阵.为此,设计了一种基于共轭梯度法的感知矩阵优化方法,该方法简单易行,且所求结果的Gram矩阵与目标Gram矩阵更接近.实验结果表明,此感知矩阵优化方法在理论分析、实际图像应用及算法有效性上均具优势.
AbstractList TP399; 压缩感知理论中降低信号维数的关键问题是构造有效的测量矩阵.在已知稀疏基的情况下,基于ETF(Equiangular Tight Frame)框架的测量矩阵构造方法和稀疏信号重构过程均依赖于感知矩阵.为此,设计了一种基于共轭梯度法的感知矩阵优化方法,该方法简单易行,且所求结果的Gram矩阵与目标Gram矩阵更接近.实验结果表明,此感知矩阵优化方法在理论分析、实际图像应用及算法有效性上均具优势.
Author 刘三阳
李昕艺
谢维
AuthorAffiliation 西安电子科技大学 数学与统计学院,陕西 西安,710126
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LI Xinyi
LIU Sanyang
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共轭梯度法
压缩感知
ETF框架
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Title 基于共轭梯度法的感知矩阵优化方法
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