Convergence and performance of the peeling wavelet denoising algorithm

Céline Lacaux (), Aurélie Muller-Gueudin (), Radu Ranta () and Samy Tindel ()

Metrika: International Journal for Theoretical and Applied Statistics, 2014, vol. 77, issue 4, 509-537

Abstract: This note is devoted to an analysis of the so-called peeling algorithm in wavelet denoising. Assuming that the wavelet coefficients of the useful signal are modeled by generalized Gaussian random variables and its noisy part by independent Gaussian variables, we compute a critical thresholding constant for the algorithm, which depends on the shape parameter of the generalized Gaussian distribution. We also quantify the optimal number of steps which have to be performed, and analyze the convergence of the algorithm. Several implementations are tested against classical wavelet denoising procedures on benchmark and simulated biological signals. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Wavelets; Denoising; Peeling algorithm; Empirical processes; Generalized Gaussian distribution (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s00184-013-0451-y

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