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Hidden Markov Models for Wavelet-based Signal Processing

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Title: Hidden Markov Models for Wavelet-based Signal Processing
Author: Crouse, Matthew; Baraniuk, Richard G.; Nowak, Robert David
Type: Conference Paper
Citation: M. Crouse, R. G. Baraniuk and R. D. Nowak,"Hidden Markov Models for Wavelet-based Signal Processing," in Asilomar Conference on Signals, Systems, and Computers,, pp. 1029-1035.
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.
Date Published: 1996-11-01

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  • ECE Publications [1032 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.