A Bayesian Super-Resolution Approach to Demosaicing of Blurred Images
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A Bayesian Super-Resolution Approach to Demosaicing of Blurred Images Miguel Vega,1 Rafael Molina,2 and Aggelos K. Katsaggelos3 1 Departamento
de Lenguajes y Sistemas Inform´aticos, Escuela T´ecnica Superior de Ingenier´ıa Infom´atica, Universidad de Granada, 18071 Granada, Spain 2 Departamento de Ciencias de la Computaci´ on e Inteligencia Artificial, Escuela T´ecnica Superior de Ingenier´ıa Infom´atica, Universidad de Granada, 18071 Granada, Spain 3 Department of Electrical Engineering and Computer Science, Robert R. McCormick School of Engineering and Applied Science, Northwestern University, Evanston, IL 60208-3118, USA Received 10 December 2004; Revised 6 May 2005; Accepted 18 May 2005 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. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
1.
INTRODUCTION
Most digital color cameras use a single coupled charge device (CCD), or a single CMOS sensor, with a color filter array (CFA) to acquire color images. Unfortunately, the color filter generates different spectral responses at every CCD cell. The most widely used CFA is the Bayer one [1]. It imposes a spatial pattern of two G cells, one R, and one B cell, as shown in Figure 1. Bayer camera pixels convey incomplete color information which needs to be extended to produce a visible color image. Such color processing is known as demosaicing (or demosaicking). From the pioneering work of Bayer [1] to nowadays, a lot of work has been devoted to the demosaicing topic (see [2] for a review). The use of a CFA and the corresponding demosaicing process produce undesirable artifacts, which are difficult to avoid. Among such artifacts are the zipper effect, also known as color fringe, and the appearance of moir´e patterns. Different interpolation techniques have been applied to demosaicing. Cok [3] applied bilinear interpolation to the G channel first, since it is the most populated and is supposed to apport information about luminance, and then applied bilinear interpolation to the chrominance ratios R/G and B/G. Freeman [4] applied a median filter to the differences between bilineraly interpolated values of the different channels, and based on these and the observed channel at every pixel,
the intensities of the two other channels are estimated. An improvement of this technique was to perform adaptive interpolation considering chrominance gradients, so as to take into account edges between objects [5]. This technique was further improved i
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