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In this paper, we propose residual interpolation (RI) as an alternative to color difference interpolation, which is a widely accepted technique for color image demosaicking. Our proposed RI performs the interpolation in a residual domain, where the residuals are differences between observed and tentatively estimated pixel values. Our hypothesis for the RI is that if image interpolation is performed in a domain with a smaller Laplacian energy, its accuracy is improved. Based on the hypothesis, we estimate the tentative pixel values to minimize the Laplacian energy of the residuals. We incorporate the RI into the gradient-based threshold free algorithm, which is one of the state-of-the-art Bayer demosaicking algorithms. Experimental results demonstrate that our proposed demosaicking algorithm using the RI surpasses the state-of-the-art algorithms for the Kodak, the IMAX, and the beyond Kodak data sets.

Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking.

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