UM
Signal denoising using wavelets and block hidden Markov model
Liao Z.3; Tang Y.Y.5
2005-08-01
Source PublicationInternational Journal of Pattern Recognition and Artificial Intelligence
ISSN02180014
Volume19Issue:5Pages:681-700
AbstractThis paper presents a new framework for signal denoising based on wavelet-domain hidden Markov models (HMMs). The new framework enables us to concisely model the statistical dependencies and non-Gaussian statistics encountered in real-world signals, and enables us to get a more reliable and local model using blocks. Wavelet-domain HMMs are designed with the intrinsic properties of wavelet transform and provide powerful yet tractable probabilistic signal models. In this paper, we propose a novel wavelet domain HMM using blocks to strike a delicate balance between improving spatial adaptability of contextual HMM (CHMM) and modeling a more reliable HMM. Each wavelet coefficient is modeled as a Gaussian mixture model, and the dependencies among wavelet coefficients in each subband are described by a context structure, then the structure is modified by blocks which are connected areas in a scale conditioned on the same context. Before denoising a signal, efficient Expectation Maximization (EM) algorithms are developed for fitting the HMMs to observational signal data. Parameters of trained HMM are used to modify wavelet coefficients according to the rule of minimizing the mean squared error (MSE) of the signal. Then, reverse wavelet transformation is utilized to modified wavelet coefficients. Finally, experimental results are given. The results show that block hidden Markov model (BHMM) is a powerful yet simple tool in signal denoising. © World Scientific Publishing Company.
KeywordAdditive Gaussian white noise Block hidden markov model Wavelet transform
DOI10.1142/S0218001405004265
URLView the original
Language英語
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Cited Times [WOS]:5   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Concprdoa University
2.The University of Hong Kong
3.University of Electronic Science and Technology of China
4.Hong Kong Baptist University
5.Chongqing University
Recommended Citation
GB/T 7714
Liao Z.,Tang Y.Y.. Signal denoising using wavelets and block hidden Markov model[J]. International Journal of Pattern Recognition and Artificial Intelligence,2005,19(5):681-700.
APA Liao Z.,&Tang Y.Y..(2005).Signal denoising using wavelets and block hidden Markov model.International Journal of Pattern Recognition and Artificial Intelligence,19(5),681-700.
MLA Liao Z.,et al."Signal denoising using wavelets and block hidden Markov model".International Journal of Pattern Recognition and Artificial Intelligence 19.5(2005):681-700.
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