Accession Number:

ADA458897

Title:

Robust Wavelet Thresholding for Noise Suppression

Descriptive Note:

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS

Personal Author(s):

Report Date:

1996-12-01

Pagination or Media Count:

6.0

Abstract:

Approaches to wavelet-based denoising or signal enhancement have so far relied on the assumption of normally distributed perturbations To relax this assumption, which is often violated in practice, we derive a robust wavelet thresholding technique based on the Minimax Description Length principle. We first determine the least favorable distribution in the epsilon-contaminated normal family as the member that maximizes the entropy. We show that this distribution and the best estimate based upon it, namely the Maximum Likelihood Estimate constitute a saddle point. This results in a threshold that is more resistant to heavy-tailed noise, but for which the estimation error is still potentially unbounded We address the practical case where the underlying signal is known to be bounded, and derive a two-sided thresholding technique that is resistant to outliers and has bounded error. We provide illustrative examples.

Subject Categories:

  • Acoustics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE