Convergence and performances of the peeling wavelet denoising algorithm
Statistics Theory
2009-11-23 v1 Statistics Theory
Abstract
This note is devoted to an analysis of the so-called peeling algorithm in wavelet denoising. Assuming that the wavelet coefficients of the signal can be modeled by generalized Gaussian random 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 versions of the obtained algorithm were implemented and tested against classical wavelet denoising procedures on benchmark and simulated biological signals.
Cite
@article{arxiv.0911.4097,
title = {Convergence and performances of the peeling wavelet denoising algorithm},
author = {Céline Lacaux and Aurélie Muller and Radu Ranta and Samy Tindel},
journal= {arXiv preprint arXiv:0911.4097},
year = {2009}
}
Comments
20 pages