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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 |
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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.
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Received: 26 March 2010
Revised: 20 December 2010
Accepted manuscript online:
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PACS:
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05.40.Ca
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(Noise)
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05.45.-a
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(Nonlinear dynamics and chaos)
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05.45.Tp
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(Time series analysis)
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Fund: 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). |
Cite this article:
Xie Zong-Bo(谢宗伯) and Feng Jiu-Chao(冯久超) Denoising via truncated sparse decomposition 2011 Chin. Phys. B 20 050504
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