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Non-blind Image Deblurring from a Single Image

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Abstract

Conventional non-blind image deblurring algorithms often involve in maximum a posteriori (MAP) estimation and natural image priors. However, MAP estimation has several disadvantages which limit its application. To address these issues, we propose to use Bayesian minimum mean squared error (MMSE) estimation instead of MAP to perform deblurring. The new method is based on high-order non-local range–Markov random field (NLR-MRF) prior, which is an effective statistical framework to model prior knowledge of natural images. The high-order NLR-MRF prior can be integrated into MMSE framework naturally. Then, an efficient Gibbs sampling algorithm is employed to compute MMSE estimation. For convenience of computation, we convert to solve a least-squares problem for sampling latent sharp images. The proposed method frees the users from determining regularization parameter beforehand, which relies on unknown noise level. Both quantitative and qualitative evaluations show superior or comparable results to the state-of-the-art deblurring methods.

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Acknowledgments

We would like to thank Fergus et al., Krishnan, Levin et al., Schmidt et al., Shan et al., and other authors for providing their part of works on the Internet. This work was supported in part by the Natural Science Foundation of China under Grant No. 90924026, 81071141, and 60873114, the Fundamental Research of 12th five years Plan Project of China under Grant No. 0101050302, the Key Projects of Knowledge Innovation Program at CAS under Grant No. Y1S6051S31, the Advanced Research Projects under Grant No. 5130101.

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Authors and Affiliations

  1. The State Key Laboratory of Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Bo Zhao, Wensheng Zhang, Huan Ding & Hu Wang

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  1. Bo Zhao
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  2. Wensheng Zhang
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  3. Huan Ding
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  4. Hu Wang
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Correspondence to Bo Zhao.

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Zhao, B., Zhang, W., Ding, H. et al. Non-blind Image Deblurring from a Single Image. Cogn Comput 5, 3–12 (2013). https://doi.org/10.1007/s12559-012-9139-2

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