Variational Bayesian image restoration with a product of spatially weighted total variation image priors

Authors:
Giannis Chantas;Nikolaos P. Galatsanos;Rafael Molina;Aggelos K. Katsaggelos
Affiliations:
Department of Computer Science, University of Ioannina, Greece;Department of Electrical Engineering, University of Patras Rio, Greece;Departamento de Ciencias de la Computación e I. A., Universidad de Granada, Granada, Spain;Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL
Venue:
IEEE Transactions on Image Processing
Year:
2010

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Abstract

In this paper, a new image prior is introduced and used in image restoration. This prior is based on products of spatially weighted total variations (TV). These spatial weights provide this prior with the flexibility to better capture local image features than previous TV based priors. Bayesian inference is used for image restoration with this prior via the variational approximation. The proposed restoration algorithm is fully automatic in the sense that all necessary parameters are estimated from the data and is faster than previous similar algorithms. Numerical experiments are shown which demonstrate that image restoration based on this prior compares favorably with previous state-of-the-art restoration algorithms.