压缩感知的冗余字典及其迭代软阈值实现算法
Redundant dictionaries of compressed sensing and an application algorithm of iterative soft threshold
赵慧民 1倪霄2
作者信息
- 1. 广东技术师范学院电子与信息学院,广东广州510665
- 2. 冠微科技(深圳)有限公司,广东深圳518125
- 折叠
摘要
冗余字典的信号稀疏分解是一种新的信号表示理论,采用超完备的冗余函数系统代替传统的正交基函数,为信号自适应地稀疏扩展提供了极大的灵活性.本文研究了压缩感知理论下的冗余字典、测量矩阵及其限制等容特性(RIP,Restricted Isometry Property),并给出了RIP、字典大小、稀疏度和测量次数的关系,提出了一种新的迭代软阈值(IST)算法,与正交匹配追踪(OMP)算法和迭代硬阈值(IHT)算法相比较,实验结果表明了IST算法具有更高的信号恢复率.
Abstract
Signal sparse decomposition of redundant dictionaries is a new theory for signal representation.The theory can adaptively provide a flexible method for signal sparsity extension via using overcomplete redundant function instead of conventional orthonormal-basis function.Based on investigation for redundant dictionaries and measurement matrix as well as restricted isometry property for compressed sensing,the paper presents a novel algorithm of iterative soft threshold (IST).Comparing the proposed IST with orthogonal matching pursuits (OMP) and iterative hard threshold (IHT) respectively,experiment results show that the IST algorithm has higher signal recovery ratio.
关键词
压缩感知/冗余字典/迭代阈值算法/限制等容特性/测量矩阵Key words
compressed sensing/redundant dictionaries/iterative threshold algorithm/Restricted Isometry Property/measurement matrix引用本文复制引用
基金项目
国家自然科学基金(61272381)
广东省自然科学基金(S2012010008639)
广东省科技计划项目(2012B010100035)
出版年
2013
NETL
NSTL
维普
万方数据