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Conventional approach in single-chip digital cameras is a use of color filter arrays (CFA) in order to sample different spectral components. Demosaicing algorithms interpolate these data to complete red, green, and blue values for each image pixel, in order to produce an RGB image. In this paper we propose a novel demosaicing algorithm for the Bayer CFA. It is assumed that the initial estimates of color channels contain two additive components: the true values of color intensities and the errors. The errors are considered as an additive noise, and often called as a demosaicing noise, that has been removed. However, this noise is not white and strongly depends on a signal. Usually, the intensity of this noise is higher near edges of image details. We use spatially designed signal-adaptive filter to remove the noise. This filter is based on the local polynomial approximation (LPA) and the paradigm of the intersection of confidence intervals (ICI) applied for selection adaptively varying scales (window sizes) of LPA. The LPA-ICI technique is nonlinear and spatially-adaptive with respect to the smoothness and irregularities of the image. The efficiency of the proposed approach is demonstrated by simulation results.
Paliy et al. (Thu,) studied this question.