Hidden Markov models for wavelet-based signed processing

Matthew S. Crouse, Richard G. Baraniuk, Robert D. Nowak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

Current wavelet-based statistical signal and image processing techniques such as shrinkage and filtering treat the wavelet coefficients as though they were statistically independent. This assumption is unrealistic; considering the statistical dependencies between wavelet coefficients can yield substantial performance improvements. In this paper, we develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet coefficients. To illustrate the power of the new framework, we derive a new signal denoising algorithm that outperforms current scalar shrinkage techniques.

Original languageEnglish (US)
Title of host publicationConference Record of the Asilomar Conference on Signals, Systems and Computers
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1029-1035
Number of pages7
Volume2
StatePublished - 1997
EventProceedings of the 1996 30th Asilomar Conference on Signals, Systems & Computers. Part 2 (of 2) - Pacific Grove, CA, USA
Duration: Nov 3 1996Nov 6 1996

Other

OtherProceedings of the 1996 30th Asilomar Conference on Signals, Systems & Computers. Part 2 (of 2)
CityPacific Grove, CA, USA
Period11/3/9611/6/96

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Electrical and Electronic Engineering

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