Authors:
Dimitris Tzikas
;
Aristidis Likas
and
Nikolaos Galatsanos
Affiliation:
University of Ioannina, Greece
Keyword(s):
Bayesian, Variational, Blind Deconvolution, Kernel Prior, Sparse Prior, Robust Prior, Student-t Prior.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Enhancement and Restoration
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Statistical Approach
Abstract:
In this paper we present a new Bayesian model for the blind image deconvolution (BID) problem. The main novelties of this model are two. First, a sparse kernel based representation of the point spread function (PSF) that allows for the first time estimation of both PSF shape and support. Second, a non Gaussian heavy tail prior
for the model noise to make it robust to large errors encountered in BID when little prior knowledge is available about both image and PSF. Sparseness and robustness are achieved by introducing Student-t priors both for the PSF and the noise. A Variational methodology is proposed to solve this Bayesian model. Numerical experiments are presented both with real and simulated data that demonstrate the advantages of this model as compared to previous Gaussian based ones.