Statistical signal processing using wavelet-domain hidden Markov models

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

doi:10.1117/12.279689

30 October 1997 Statistical signal processing using wavelet-domain hidden Markov models

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

Proceedings Volume 3169, Wavelet Applications in Signal and Image Processing V; (1997) https://doi.org/10.1117/12.279689
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

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.

Citation Download Citation

Matthew S. Crouse, Robert D. Nowak, and Richard G. Baraniuk "Statistical signal processing using wavelet-domain hidden Markov models", Proc. SPIE 3169, Wavelet Applications in Signal and Image Processing V, (30 October 1997); https://doi.org/10.1117/12.279689

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