Statistical signal processing usin wavelet-domain hidden markov models

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

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

2 Scopus citations

Abstract

Most wavelet-based statistical signal and image processing techniques 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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Pages248-259
Number of pages12
Volume3169
DOIs
StatePublished - 1997
EventWavelet Applications in Signal and Image Processing V - San Diego, CA, United States
Duration: Jul 30 1997Jul 30 1997

Other

OtherWavelet Applications in Signal and Image Processing V
CountryUnited States
CitySan Diego, CA
Period7/30/977/30/97

Keywords

  • Estimation
  • Hidden markov models
  • Wavelets

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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