基于小波域维纳滤波器的信号稀疏表示

doi:10.3969/j.issn.0255-8297.2012.06.006

应用科学学报 ›› 2012, Vol. 30 ›› Issue (6): 595-600.doi: 10.3969/j.issn.0255-8297.2012.06.006

基于小波域维纳滤波器的信号稀疏表示

赵志鹏1,2，岑翼刚1,2，陈晓方3

1. 北京交通大学信息科学研究所，北京100044
2. 现代信息科学与网络技术北京市重点实验室，北京100044
3. 中南大学信息科学与工程学院，长沙410083

收稿日期:2011-05-06 修回日期:2011-11-25 出版日期:2012-11-27 发布日期:2011-11-25
通信作者: 陈晓方，副教授，研究方向: 复杂系统建模与优化控制、遗传算法、原料供需链优化、信号处理等，E-mail: xiaofangchen@csu.edu.cn
基金资助:
国家自然科学基金(No.61272028, No.60802045, No.61073079)；北京市自然科学基金预探索项目基金(No.4113075)；中央高校基本科研业务费专项资金(No.2009JBM028, No.2011JBM223, No.201012200110)；教育部博士点新教师基金(No.20100162120019)；北京交通大学红果园D类人才支持计划资助

Sparse Representation of Signals Based on Wavelet Domain Wiener Filtering

ZHAO Zhi-peng1,2, CEN Yi-gang1,2, CHEN Xiao-fang3

1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China
3. School of Information Science and Engineering, Central South University, Changsha 410083, China

Received:2011-05-06 Revised:2011-11-25 Online:2012-11-27 Published:2011-11-25

摘要/Abstract

摘要： 经典小波分解对信号稀疏化效果不佳，为此设计了基于小波域经验维纳滤波器的稀疏表示算法. 该算法可自适应地衰减每个小波系数，增大系数的稀疏度及可压缩性，从而提高压缩感知算法对信号的恢复质量. 仿真结果表明，与传统的基于小波变换的信号稀疏表示及恢复算法相比，该算法较大地提升了对信号及图像的恢复质量.

关键词: 稀疏表示, 维纳滤波器, 小波变换, 正交匹配追踪算法

Abstract: A wavelet-based Wiener filter is proposed for signal sparse representation since the classical wavelet transform can not posses good sparse results for real signals. The proposed method can adaptively decrease the magnitude of each wavelet coefficient so that sparsity and compressibility of the wavelet coefficients is improved. This results in improvement of recovered signal quality of the compressed sensing algorithm. Simulation results show that, compared to the original sparse representation based on wavelet transform, the proposed algorithm can significantly improve quality of recovered signals for both signals and images.

Key words: sparse representation, Wiener filter, wavelet transform, orthogonal matching pursuit (OMP)algorithm

中图分类号:

TN911.72

赵志鹏1,2，岑翼刚1,2，陈晓方3. 基于小波域维纳滤波器的信号稀疏表示[J]. 应用科学学报, 2012, 30(6): 595-600.

ZHAO Zhi-peng1,2, CEN Yi-gang1,2, CHEN Xiao-fang3. Sparse Representation of Signals Based on Wavelet Domain Wiener Filtering[J]. Journal of Applied Sciences, 2012, 30(6): 595-600.

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基于小波域维纳滤波器的信号稀疏表示

Sparse Representation of Signals Based on Wavelet Domain Wiener Filtering

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