Image Restoration Based on Wavelet-Domain Contextual Hidden Markov Tree Model

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外文摘要：From the viewpoint of Bayesian method， image restoration algorithms based on wavelet-domain hidden Markov tree (HMT) model have been proposed recently。 These algorithms utilize the HMT model which captures the persistence property of wavelet coefficients， but lack the clustering property of wavelet coefficients within a scale。 In this paper， we propose a new image restoration algorithm。 The algorithm specifies the prior distribution of real-world images through wavelet-domain contextual hidden Markov tree (CHMT) model which enhances the clustering property of the HMT model by adding extended coefficients associated with wavelet coefficients and converts the restoration problem to a constrained optimization task。 Experimental results show that， the proposed algorithm produces almost better results than the HMT model produces for image restoration， both in objective and subjective qualities。

外文关键词：

Hidden Markov modelsImage restorationContext modelingClustering algorithmsWavelet coefficientsDegradationBayesian methodsComputer scienceSoftware engineeringAutomation

作者：

Lou Shuai、Ding Zhenliang、Yuan Feng、Li Jing

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作者单位：

Sch. of Electr. Eng. & Autom., Harbin Inst. of Technol., Harbin, China

会议名称：

International Conference on Computer Science and Software Engineering;CSSE 2008

会议地点：

Wuhan(CN)

会议母体文献：

Computer Science and Software Engineering. 2008

页码：

177-180

出版时间：

2008

DOI：

10.1109/CSSE.2008.655