Parsimony and Wavelet Methods for Denoising

Abstract

Some wavelet-based methods for signal estimation in the presence of noise are reviewed in the context of the parsimonious representation of the underlying signal. Three approaches are considered. The first is based on the application of the MDL principle. The robustness of this method is improved in the second approach, by relaxing the assumption of known noise distribution following Huber's work. In the third approach, a Bayesian strategy is adopted in order to incorporate prior information pertaining to the signal of interest; this method is especially useful at low signal-to-noise ratios.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1998
Accession Number
ADA459557

Entities

People

  • Hamid Krim
  • I. C. Schick
  • J.-c. Pesquet

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Bayesian Networks
  • Coefficients
  • Computer Programming
  • Data Science
  • Gaussian Distributions
  • Identification
  • Models
  • Noise
  • Normal Distribution
  • Pattern Recognition
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Signal Processing
  • Standards
  • Statistics

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Computer Vision.
  • Statistical inference.

Technology Areas

  • AI & ML