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Title:
high-order mrf prior based bayesian deblurring
Author: Zhao Bo ; Zhang Wensheng ; Liu Jin ; Ding Huan
Keyword: Estimation ; Mean square error
Source: International Journal of Digital Content Technology and its Applications
Issued Date: 2011
Volume: 5, Issue:12, Pages:383-392
Indexed Type: EI
Department: (1) State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China; (2) State Key Laboratory of Software Engineering Computer School Wuhan University China
Abstract: A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF provides an effective framework to model the statistical prior of natural images and leads to excellent performance in the application of image denoising and inpainting. Moreover, the framework will be extended to image deblurring in our work. Instead of commonly used maximum a-posteriori (MAP) estimation, which has several shortcomings, the high-order NLR-MRF prior is integrated into Bayesian minimum mean squared error (MMSE) estimation framework. Then, an efficient Gibbs sampling algorithm is adopted to compute MMSE estimation. The proposed method frees the user from determining regularization parameter beforehand, which relies on unknown noise level. We perform experiments on synthetic and real-world data to demonstrate the effectiveness of our method. Both quantitatively and qualitatively evaluations show superior or comparable results to the state-of-art deblurring methods.
English Abstract: A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF provides an effective framework to model the statistical prior of natural images and leads to excellent performance in the application of image denoising and inpainting. Moreover, the framework will be extended to image deblurring in our work. Instead of commonly used maximum a-posteriori (MAP) estimation, which has several shortcomings, the high-order NLR-MRF prior is integrated into Bayesian minimum mean squared error (MMSE) estimation framework. Then, an efficient Gibbs sampling algorithm is adopted to compute MMSE estimation. The proposed method frees the user from determining regularization parameter beforehand, which relies on unknown noise level. We perform experiments on synthetic and real-world data to demonstrate the effectiveness of our method. Both quantitatively and qualitatively evaluations show superior or comparable results to the state-of-art deblurring methods.
Language: 英语
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16172
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Zhao Bo,Zhang Wensheng,Liu Jin,et al. high-order mrf prior based bayesian deblurring[J]. International Journal of Digital Content Technology and its Applications,2011-01-01,5(12):383-392.
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