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.
Highlights
A Bayesian Super-Resolution Approach to Demosaicing of Blurred ImagesMost of the available digital color cameras use a single image sensor with a color filter array (CFA) in acquiring an image
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
We can apply the theory developed in [23, 26], by taking into account that we are dealing with multichannel images, and the relationship between channels has to be included in the deconvolution process [25]
Summary
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 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
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