Robust color demosaicking with adaptation to varying spectral correlations

Almost all existing color demosaicking algorithms for digital cameras are designed on the assumption of high correlation between red, green, blue (or some other primary color) bands. They exploit spectral correlations between the primary color bands to interpolate the missing color samples, but in areas of no or weak spectral correlations, these algorithms are prone to large interpolation errors. Such demosaicking errors are visually objectionable because they tend to correlate with object boundaries and edges. This paper proposes a remedy to the above problem that has long been overlooked in the literature. The main contribution of this work is a hybrid demosaicking approach that supplements an existing color demosaicking algorithm by combining its results with those of adaptive intraband interpolation. This is formulated as an optimal data fusion problem, and two solutions are proposed: one is based on linear minimum mean-square estimation and the other based on support vector regression. Experimental results demonstrate that the new hybrid approach is more robust and eliminates the worst type of color artifacts of existing color demosaicking methods.