Robust Wavelet Thresholding for Noise Suppression
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1996
- Accession Number
- ADA458897
Entities
People
- Hamid Krim
- I. C. Schick
Organizations
- Massachusetts Institute of Technology