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   -> Volume 6, Issue 2

Preprint: Comparison of wavelet and wavelet packet denoising algorithms
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Markus Kraft (

PostPosted: Fri Jan 31, 1997 1:13 am    
Subject: Preprint: Comparison of wavelet and wavelet packet denoising algorithms
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#6 Preprint: Comparison of wavelet and wavelet packet denoising algorithms

I would like to make the following article available to wavelet digest

Title: Comparison and Assessment of Various Wavelet and Wavelet Packet
based Denoising Algorithms for Noisy Data

Authors: F. Hess and M. Kraft

Abstract: In the recent past a variety of denoising methods based on
wavelet and wavelet packet analysis have been introduced and
investigated. In this work some of these methods have been compared
with each other in order to give a user an idea which of these methods
suits best for a particular problem. One field of application is data
analysis of neurophysiological signals. In the test case presented,
white noise is superimposed to a noise free neurophysiological signal,
containing different local structures. Then in a Monte-Carlo
simulation linear wavelet and wavelet packet thresholding, non-linear
soft and hard thresholding for wavelets (e.g. Donoho) and wavelet
packets (Donoho and Johnstone), the coherence denoising algorithm
(e.g. Wickerhauser) are compared with each other (with respect to
different norms) and tested versus several parameters. For example
the influence of the choice of the basis function, choice of the cost
function in case of wavelet packets, of the type of noise have been
investigated. For the neurophysiological signal under investigation it
was found that wavelet nonlinear hard thresholding performed
best. Additionally, it is demonstrated that a scale dependent
threshold can improve this result significantly.

A copy can be downloaded from

or by contacting via the following address:
Markus Kraft
Fachbereich Chemie
Universitaet Kaiserslautern
D 67663 Kaiserslautern
All times are GMT + 1 Hour
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