Hidden Markov models for wavelet-based signal processing
- 12 June 2006
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1029-1035
- https://doi.org/10.1109/acssc.1996.599100
Abstract
Conference PaperCurrent 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 techniquesKeywords
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