A Method Using Nonparametric Hidden Markov Trees for Image Denoising

被引：2

作者：

Wang Song ^{[1
]}

Wang Weihong ^{[1
]}

机构：

[1] Zhejiang Univ Technol, Coll Software, Hangzhou, Zhejiang, Peoples R China

来源：

PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND NATURAL COMPUTING, VOL I | 2009年

关键词：

hidden Markov trees; nonparametric Bayesian methods; image denoising;

D O I：

10.1109/CINC.2009.49

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：143 / 146

页数：4

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