Hidden Markov Models for Wavelet-based Signal Processing
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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.
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M. Crouse, R. G. Baraniuk and R. D. Nowak, "Hidden Markov Models for Wavelet-based Signal Processing," vol. 2, 1996.