NDLI: Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation

Content Provider	IEEE Xplore Digital Library
Author	Molina, R. Mateos, J. Katsaggelos, A.K.
Copyright Year	1992
Abstract	Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the image and blur, and gamma distributions for the unknown parameters (hyperparameters) of the priors and the image formation noise. We show how the gamma distributions on the unknown hyperparameters can be used to prevent the proposed blind deconvolution method from converging to undesirable image and blur estimates and also how these distributions can be inferred in realistic situations. We apply variational methods to approximate the posterior probability of the unknown image, blur, and hyperparameters and propose two different approximations of the posterior distribution. One of these approximations coincides with a classical blind deconvolution method. The proposed algorithms are tested experimentally and compared with existing blind deconvolution methods
Sponsorship	IEEE Signal Processing Society
Page Count	13
File Size	3527324
Starting Page	3715
Ending Page	3727
File Format	PDF
ISSN	10577149
Volume Number	15
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deconvolution Image restoration Bayesian methods Degradation Parameter estimation Ultrasonic imaging Image converters Testing Convolution Equations variational methods Bayesian framework blind deconvolution parameter estimation
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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