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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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| [1] |
Chin T J and Suter D 2007 IEEE Trans. Image Process. 16 1662
|
| [2] |
Balster E J, Zheng Y F and Ewing R L 2006 IEEE Trans. Circuits Syst. Video Technol. 16 220
|
| [3] |
Han J J and Fu W J 2010 Chin. Phys. B bf 19 010205
|
| [4] |
Hoyer P O and Hyvuarinen A 2002 Vision Research 42 1593
|
| [5] |
Manfredi C, Aniello M D and Bruscaglioni P 2001 Logopedics Phoniatrics Vocology 26 179
|
| [6] |
Li E Z, Ling B L, Liu Y, Ti A, Hu L Q and Gao X 2010 Chin. Phys. B bf 19 035203
|
| [7] |
Onn R, Steinhardt A O and Bojanczyk A W 1991 IEEE Trans. Signal Process. 39 1575
|
| [8] |
Zhang Z Y and Zha H Y 2004 Science in China Ser. A Mathematics 47 908
|
| [9] |
Zhang Z, Zhang H, Sang H and Yuan W 2008 Proc. 2008 Congress on Image and Signal Processing 1 411
|
| [10] |
Momania S and Odibatb Z 2007 Phys. Lett. A 365 345
|
| [11] |
Odibata Z and Momanib S 2008 Appl. Math. Modelling 32 28
|
| [12] |
Xie Z B and Feng J C 2009 Chin. Phys. Lett. 26 030501
|
| [13] |
Liu Z C 2009 Chin. Phys. B bf 18 636
|
| [14] |
Cui D, Gao Z Y and Zhao X M 2008 Chin. Phys. B bf 17 1703
|
| [15] |
Xie Y H, Yang W and Zhang Y 2010 Acta Phys. Sin. bf 59 8255 (in Chinese) endfootnotesize
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