Wavelet De-Noising Based on Genetic Adaptive Threshold Algorithm

Guang Wen Wu; Chang Ming Wang

doi:10.4028/www.scientific.net/AMR.989-994.3595

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Wavelet De-Noising Based on Genetic Adaptive...

Wavelet De-Noising Based on Genetic Adaptive Threshold Algorithm

Abstract:

The central step of de-noising in wavelet domain is the threshold selection. To get the best de-noising result, the threshold should be selected according to the noisy observation. This paper presents a genetic adaptive threshold method, which gets the optimum threshold in the sense of least MSE by using an estimation function of the signals’ MSE function. The simulation results with standard wavelet test signals shows that the operation speed of the proposed method four times the traditional continuous searching algorithm, moreover, the optimum threshold calculated is more accurate and reasonable.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

3595-3598

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.3595

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Online since:

July 2014

Authors:

Guang Wen Wu*, Chang Ming Wang

Keywords:

De-Noising, Genetic Algorithm (GA), Threshold, Wavelet

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* - Corresponding Author

References

[1] D. Donoho. De-noising by Soft-thresholding [J]. IEEE Trans. on IT. , 1995, 41(3): 613-627.

DOI: 10.1109/18.382009

Google Scholar

[2] Jian Li, Changkui Cheng, Tianyan Jiang. Wavelet De-noising of Partial Discharge Signals Based on Genetic Adaptive Threshold Estimation [J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2012, 19: 543-549.

DOI: 10.1109/tdei.2012.6180248

Google Scholar

[3] Tahsina Farah Sanam, Celia Shahnaz. Noisy speech enhancement based on an adaptive threshold and a modified hard thresholding function in wavelet packet domain [J]. Digital Signal Processing, 2013 , 23: 941-951.

DOI: 10.1016/j.dsp.2012.12.001

Google Scholar

[4] Mehdi Nasri, Hossein Nezamabai-pour. Image denoising in the wavelet domain using a new adaptive thresholding function [J]. Neurocomputing , 2009, 72: 1012-1025.

DOI: 10.1016/j.neucom.2008.04.016

Google Scholar

[5] W.Y. Liu, J.G. Han. The optimal Mexican hat wavelet filter de-noising method based on cross-validation method [J]. Neurocomputing, 2013 , 108: 31-35.

DOI: 10.1016/j.neucom.2012.11.010

Google Scholar

[6] M. Beenamol, S. Prabavathy, J. Mohanalin. Wavelet based seismic signal de-noising using Shannon and Tsallis entropy [J]. Computers and Mathematics with Applications, 2012 , 64: 3580-3593.

DOI: 10.1016/j.camwa.2012.09.009

Google Scholar

[7] Zhang L, Pan Q, Zhang H C, etc. On the determination of threshold in threshold-based de-noising by wavelet transform [J]. ACTA ELECTRONICA SINICA, 2001, 29(3): 400-402.

Google Scholar

[8] D.L. Donoho, I.M. Johnstone, Ideal spatial adaption by wavelet shrinkage, Biometrika 81 (3) (1994) 425-455.

DOI: 10.1093/biomet/81.3.425

Google Scholar