Image denoising with nonparametric hidden Markov trees

被引：0
|
作者：
Kivinen, Jyri J. [1 ,2 ]
Sudderth, Erik B. [3 ]
Jordan, Michael I. [3 ,4 ]
机构：
[1] Aalto Univ, FIN-02150 Espoo, Finland
[2] Int Comp Sci Inst, Berkeley, CA USA
[3] Univ Calif Berkeley, Div Comp Sci, Berkeley, CA USA
[4] Univ Calif Berkeley, Dept Stat, Berkeley, CA USA
来源：
2007 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-7 | 2007年
关键词：
hidden Markov trees; hierarchical Dirichlet processes; nonparametric Bayesian methods; wavelet transforms; image denoising;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We develop a hierarchical, nonparametric statistical model for wavelet representations of natural images. 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
收藏
页码：1249 / +
页数：2
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