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	Image Sparse Representation Based on a Nonparametric Bayesian Model
	Ding Xinghao ; Chen Xianbo ; Ding XH(丁兴号)
	2012
关键词	DICTIONARIES
英文摘要	Conference Name:3rd International Conference on Precision Instrumentation and Measurement (CPIM 2011). Conference Address: Xiangtan, PEOPLES R CHINA. Time:JUL 18-21, 2011.; In recent years there has been a growing interest in the research of image sparse representation. Sparse representation based on over-complete dictionary become another hot topic in the field of image processing. In this paper a Nonparametric Bayesian model based on hierarchical Bayesian theory is proposed. In this model a sparse spike-slab prior is imposed on sparse coefficients and the Non-parametric Bayesian techniques based on sparse image representation are considering for learning dictionary. Proposed model can learn an over-complete dictionary from original image. Furthermore, the unknown noise variance can be estimated from noisy image. As regards to the image sparse representation, proposed model obtains good sparse solution. Comparing to other state-of-the-art image sparse representation method, this model obtains better reconstruction effects.
语种	英语
出处	http://dx.doi.org/10.4028/www.scientific.net/AMM.103.109
出版者	APPL MECH MATER
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86491]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Ding Xinghao,Chen Xianbo,Ding XH. Image Sparse Representation Based on a Nonparametric Bayesian Model. 2012-01-01.

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