Denoising via truncated sparse decomposition

doi:10.1088/1674-1056/20/5/050504

中国物理B ›› 2011, Vol. 20 ›› Issue (5): 50504-050504.doi: 10.1088/1674-1056/20/5/050504

Denoising via truncated sparse decomposition

谢宗伯, 冯久超

School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China

收稿日期:2010-03-26 修回日期:2010-12-20 出版日期:2011-05-15 发布日期:2011-05-15
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 60872123), the Joint Fund of the National Natural Science Foundation and the Guangdong Provincial Natural Science Foundation (Grant No. U0835001), the Doctorate Foundation of

Denoising via truncated sparse decomposition

Xie Zong-Bo(谢宗伯) and Feng Jiu-Chao(冯久超)^†

School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China

Received:2010-03-26 Revised:2010-12-20 Online:2011-05-15 Published:2011-05-15
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 60872123), the Joint Fund of the National Natural Science Foundation and the Guangdong Provincial Natural Science Foundation (Grant No. U0835001), the Doctorate Foundation of South China University of Technology, the Post-Doc Foundation of South China University of Technology, the Basic Scientific Research Fund of South China University of Technology for Youth, the Natural Science Fund of South China University of Technology for Youth, the Natural Science Foundation of Guangdong Province, China, and the China Postdoctoral
Science Foundation (Grant No. 20100480049).

摘要/Abstract

摘要： This paper proposes a denoising algorithm called truncated sparse decomposition (TSD) algorithm, which combines the advantage of the sparse decomposition with that of the minimum energy model truncation operation. Experimental results on two real chaotic signals show that the TSD algorithm outperforms the recently reported denoising algorithms–-non-negative sparse coding and singular value decomposition based method.

Abstract: This paper proposes a denoising algorithm called truncated sparse decomposition (TSD) algorithm, which combines the advantage of the sparse decomposition with that of the minimum energy model truncation operation. Experimental results on two real chaotic signals show that the TSD algorithm outperforms the recently reported denoising algorithms–-non-negative sparse coding and singular value decomposition based method.

Key words: denoising, truncated sparse decomposition, sparse decomposition, chaotic signals

中图分类号: (Noise)

05.40.Ca

05.45.-a (Nonlinear dynamics and chaos) 05.45.Tp (Time series analysis)

谢宗伯, 冯久超. Denoising via truncated sparse decomposition[J]. 中国物理B, 2011, 20(5): 50504-050504.

Xie Zong-Bo(谢宗伯) and Feng Jiu-Chao(冯久超). Denoising via truncated sparse decomposition[J]. Chin. Phys. B, 2011, 20(5): 50504-050504.

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Denoising via truncated sparse decomposition

Denoising via truncated sparse decomposition

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