Variational Bayesian Sparse Kernel-Based Blind Image Deconvolution With Student's-t Priors

Dimitris Tzikas

doi:10.1109/TIP.2008.2011757

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Variational Bayesian Sparse Kernel-Based Blind Image Deconvolution With Student's-t Priors

2009-04-01

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Dimitris Tzikas *

Aristidis Likas

N.P. Galatsanos

DOI：10.1109/TIP.2008.2011757

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Abstract

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In this paper, we present a new Bayesian model for the blind Image deconvolution (BID) problem. The main novelty of this model is the use of a sparse kernel-based model for the point spread function (PSF) that allows estimation of both PSF shape and support. In the herein proposed approach, a robust model of the BID errors and an image prior that preserves edges of the reconstructed image are also used. Sparseness, robustness, and preservation of edges are achieved by using priors that are based on the Student's-t probability density function (PDF). This pdf, in addition to having heavy tails, Is closely related to the Gaussian and, thus, yields tractable inference algorithms. The approximate variational inference methodology is used to solve the corresponding Bayesian model. Numerical experiments are presented that compare this BID methodology to previous ones using both simulated and real data.

Keywords:

Bayesian approach

blind image deconvolution (BID)

inverse problem

kernel model

sparse prior

student-t distribution

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