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	MRI Denoising Based on a Non-Parametric Bayesian Image Sparse Representation Method
	Chen, Xian-bo ; Ding, Xing-hao ; Liu, Hui ; Ding XH(丁兴号) ; Liu H(刘慧)
	2011
关键词	DICTIONARIES
英文摘要	Conference Name:International Conference on Information Science, Automation and Material System. Conference Address: Zhengzhou, PEOPLES R CHINA. Time:MAY 21-22, 2011.; Magnetic Resonance images are often corrupted by Gaussian noise which highly affects the quality of MR images. In this paper, a Non-Parametric hierarchical Bayesian image sparse representation method is proposed to wipe out Gaussian distribution noise coupling in MR images. In this method a spike-slab prior is imposed on sparse coefficients, and a redundant dictionary is learned from the corrupted image. Experimental results show that the method not only improves the effect of MRI denoising, but also can obtain good estimation of the noise variance. Compared to non-local filter method, this model shows better visual quality as well as higher PSNR.
语种	英语
出处	http://dx.doi.org/10.4028/www.scientific.net/AMR.219-220.1354
出版者	ADV MATER RES-SWITZ
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86476]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Chen, Xian-bo,Ding, Xing-hao,Liu, Hui,et al. MRI Denoising Based on a Non-Parametric Bayesian Image Sparse Representation Method. 2011-01-01.

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