NDLI: Parameter estimation in Bayesian Blind Deconvolution with super Gaussian image priors

Content Provider	IEEE Xplore Digital Library
Author	Vega, M. Molina, R. Katsaggelos, A.K.
Copyright Year	2014
Description	Author affiliation: Univ. de Granada, Granada, Spain (Vega, M.; Molina, R.) \|\| Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA (Katsaggelos, A.K.)
Abstract	Super Gaussian (SG) distributions have proven to be very powerful prior models to induce sparsity in Bayesian Blind Deconvolution (BD) problems. Their conjugate based representations make them specially attractive when Variational Bayes (VB) inference is used since their variational parameters can be calculated in closed form with the sole knowledge of the energy function of the prior model. In this work we show how the introduction in the SG distribution of a global strength (not necessary scale) parameter can be used to improve the quality of the obtained restorations as well as to introduce additional information on the global weight of the prior. A model to estimate the new unknown parameter within the Bayesian framework is provided. Experimental results, on both synthetic and real images, demonstrate the effectiveness of the proposed approach.
Starting Page	1632
Ending Page	1636
File Size	2499255
Page Count	5
File Format	PDF
ISBN	9780992862619
ISSN	20761465
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-01
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	EURASIP
Subject Keyword	Super Gaussian Histograms Deconvolution Bayesian methods Image processing Estimation Bayes methods Image restoration Electronic mail Blind deconvolution Kernel
Content Type	Text
Resource Type	Article

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