Demosaicking based on channel-correlation adaptive dictionary learning

Abstract. Image demosaicking is the problem of reconstructing color images from raw images captured using a digital camera with a color filter array. Sparse representation-based demosaicking method achieves superior performance on the commonly used Kodak dataset; however, it performs inferior on the IMAX dataset. We analyze that the factor of the sparse representation-based demosaicking methods that perform inconsistently is channel-correlation, which we define as the mean value of correlation coefficients between the RGB channels. Accordingly, we propose a channel-correlation adaptive dictionary learning-based demosaicking method. Different from the sparse representation-based demosaicking methods that use a fixed dictionary, our method trains a general dictionary on training image patches with various channel-correlations. Then, we learn a function matrix between the general dictionary and channel-correlations. For a raw image patch with an estimated channel-correlation, we compute a dictionary corresponding to its channel-correlation through the function matrix. Finally, we demosaick it with the corresponding dictionary using the sparse representation model. Experiments confirm that the proposed method performs adaptively well on raw images with various channel-correlations.