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| high-order mrf prior based bayesian deblurring | |
| Zhao Bo; Zhang Wensheng; Liu Jin; Ding Huan | |
| 2011 | |
| Source | International Journal of Digital Content Technology and its Applications
 |
| ISSN | 1975-9339 |
| Volume | 5Issue:12Pages:383-392 |
| 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.; 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. |
| Indexed Type | EI |
| Keyword | Estimation Mean Square Error |
| 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 |
| Language | 英语 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16172 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | 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,2011,5(12):383-392. |
| APA | Zhao Bo,Zhang Wensheng,Liu Jin,&Ding Huan.(2011).high-order mrf prior based bayesian deblurring.International Journal of Digital Content Technology and its Applications,5(12),383-392. |
| MLA | Zhao Bo,et al."high-order mrf prior based bayesian deblurring".International Journal of Digital Content Technology and its Applications 5.12(2011):383-392. |
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