Accession Number : ADA291668
Title : Denoising and Robust Non-Linear Wavelet Analysis,
Corporate Author : MATHSOFT INC SEATTLE WA STATISTICAL SCIENCES DIV
Personal Author(s) : Bruce, Andrew G. ; Donoho, David L. ; Gao, Hong-Ye ; Martin, R. D.
PDF Url : ADA291668
Report Date : APR 1994
Pagination or Media Count : 12
Abstract : In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting non-smooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very sensitive to certain types of non-Gaussian noise, such as outliers. In this paper, we develop outilier resistance wavelet transforms. In these transforms, outliers and outlier patches are localized to just a few scales. By using the outlier resistant wavelet transforms, we improve upon the Donoho and Johnstone nonlinear signal extraction methods. The outlier resistant wavelet algorithms are included with the S+Wavelets object-oriented toolkit for wavelet analysis.
Descriptors : *SIGNAL PROCESSING, *TIME SERIES ANALYSIS, ALGORITHMS, METHODOLOGY, GAUSSIAN NOISE, NONLINEAR SYSTEMS, EXTRACTION, NOISE, RANGE(EXTREMES), TIME DOMAIN, FREQUENCY DOMAIN.
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE