A New Approach for Optimal Decomposition Level Selection in Wavelet De-Noising

Abstract:

The paper introduces a novel algorithm to determine the optimal decomposition level in wavelet de-noising. The algorithm selects the optimal decomposition level by comparing the sparsity of wavelet coefficients at adjacent levels. The level whose wavelet coefficient has the maximum sparsity can be confirmed as the optimal decomposition level. We demonstrate experimentally that wavelet de-noising performs better using optimal decomposition level determined by our proposed algorithm than White Noise Test (WNT) method and Maximum Energy (ME) method.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 540-545
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.540
• Online since: July 2013
• Authors: Yong Xin Zhang*, Chen Li, Jian Jia, Ding Yi Fang
• Keywords: Decomposition Level, Lipschitz Exponent, Sparsity, Wavelet Transform Maximal Modulus

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