A Bayesian super-resolution approach to demosaicing of blurred images

Authors:
Miguel Vega;Rafael Molina;Aggelos K. Katsaggelos
Affiliations:
Departamento de Lenguajes y Sistemas Informáticos, Escuela Técnica Superior de Ingeniería Infomática, Universidad de Granada, Granada, Spain;Departamento de Ciencias de la Computación e Inteligencia Artificial, Escuela Técnica Superior de Ingeniería Infomática, Universidad de Granada, Granada, Spain;Department of Electrical Engineering and Computer Science, Robert R. McCormick School of Engineering and Applied Science, Northwestern University, Evanston, IL
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2006

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Abstract

Most of the available digital color cameras use a single image sensor with a color filter array (CFA) in acquiring an image. In order to produce a visible color image, a demosaicing process must be applied, which produces undesirable artifacts. An additional problem appears when the observed color image is also blurred. This paper addresses the problem of deconvolving color images observed with a single coupled charged device (CCD) from the super-resolution point of view. Utilizing the Bayesian paradigm, an estimate of the reconstructed image and the model parameters is generated. The proposed method is tested on real images.