Signal estimation using wavelet-Markov models
- 12 June 2006
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 5, 3429-3432
- https://doi.org/10.1109/icassp.1997.604601
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. 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 algorithm for signal estimation in nonGaussian noiseKeywords
This publication has 9 references indexed in Scilit:
- Hidden Markov models for wavelet-based signal processingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Bayesian approach to best basis selectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Signal de-noising using adaptive Bayesian wavelet shrinkagePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Probabilistic Independence Networks for Hidden Markov Probability ModelsNeural Computation, 1997
- Adapting to Unknown Smoothness via Wavelet ShrinkageJournal of the American Statistical Association, 1995
- Parameter estimation of dependence tree models using the EM algorithmIEEE Signal Processing Letters, 1995
- Embedded image coding using zerotrees of wavelet coefficientsIEEE Transactions on Signal Processing, 1993
- A tutorial on hidden Markov models and selected applications in speech recognitionProceedings of the IEEE, 1989
- Mixture Densities, Maximum Likelihood and the EM AlgorithmSIAM Review, 1984