NDLI: Non-blind Image Deblurring from a Single Image

Content Provider	Springer Nature Link
Author	Zhao, Bo Zhang, Wensheng Ding, Huan Wang, Hu
Copyright Year	2012
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.
Starting Page	3
Ending Page	12
Page Count	10
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	5
Issue Number	1
e-ISSN	18669964
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-04-24
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image deblurring Image prior NLR-MRF MAP estimation MMSE estimation Gibbs sampling Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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