Title :
A Method Using Nonparametric Hidden Markov Trees for Image Denoising
Author :
Song, Wang ; Weihong, Wang
Author_Institution :
Coll. of Software, Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
A hierarchical, nonparametric statistical model for wavelet representations of natural images is developed in this paper. Extending previous work on Gaussian scale mixtures, wavelet coefficients are marginally distributed according to infinite, Dirichlet process mixtures. A hidden Markov tree is then used to couple the mixture assignments at neighboring nodes. Via a Monte Carlo learning algorithm, the resulting hierarchical Dirichlet process hidden Markov tree (HDP-HMT) model automatically adapts to the complexity of different images and wavelet bases. Image denoising results demonstrate the effectiveness of this learning process.
Keywords :
Gaussian processes; Monte Carlo methods; hidden Markov models; image denoising; trees (mathematics); Dirichlet process mixtures; Gaussian scale mixtures; Monte Carlo learning algorithm; hierarchical Dirichlet process-hidden Markov tree model; hierarchical statistical model; image denoising; learning process; nonparametric hidden Markov trees; nonparametric statistical model; wavelet representations; Bayesian methods; Computational intelligence; Educational institutions; Frequency; Gaussian distribution; Hidden Markov models; Image denoising; Monte Carlo methods; Statistical distributions; Wavelet coefficients; hidden Markov trees; image denoising; nonparametric Bayesian methods;
Conference_Titel :
Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3645-3
DOI :
10.1109/CINC.2009.49
