Solving a two-colour problem by applying probabilistic approach to a full-colour multi-frame image super-resolution

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• The algorithm incorporates the two colour prior into the probabilistic model.
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• Correct modelling of the Bayer pattern in the generative process is done.
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• Super resolution and demosaicing (SRD) are joined together in MAP approach for two-colour problem.
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• SRD contributes to the quality of estimated HR images in poor conditions.

Reconstruction based algorithms play an important role in the multi-frame super-resolution problem. A group of images of the same scene are fused together to produce an image with higher spatial resolution, or with more visible details in the high spatial frequency features. Demosaicing algorithms interpolate missing pixels in a raw image taken from one Charged Coupled Device (CCD) array, upsampling the number of the pixels present in the image. Since super-resolution (SR) and demosaicing are the two faces of the same problem it is natural to address them together. In this paper it is: (i) shown that correct modelling of the Bayer pattern in the generative process improves the super-resolution performance for colour images, and (ii) an algorithm that incorporates the two colour prior into the probabilistic model is designed. The algorithm presented in this paper focuses on the classes of images that have two dominant colours, i.e. most of the areas in the image are uniformly coloured. A convex optimization procedure for joint super-resolution and demosaicing is developed which outperforms state-of-the-art algorithms.