Accession Number:

ADA291668

Title:

Denoising and Robust Non-Linear Wavelet Analysis,

Descriptive Note:

Corporate Author:

MATHSOFT INC SEATTLE WA STATISTICAL SCIENCES DIV

Report Date:

1994-04-01

Pagination or Media Count:

12.0

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 SWavelets object-oriented toolkit for wavelet analysis.

Subject Categories:

  • Statistics and Probability
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE