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To minimize cost and size, most commercial digital cameras acquire imagery using a single electronic sensor (CCD or CMOS) overlaid with a color filter array (CFA) such that each sensor pixel only samples one of the three primary color values. To restore a full-color image from CFA samples, the two missing color values at each pixel need to be estimated from the neighboring samples, a process that is commonly known as CFA demosaicking or interpolation. In this paper we present two contributions to CFA demosaicking. First, we stress the importance of well exploiting both image spatial and spectral correlations, and characterize the demosaicking artifacts due to inadequate use of either correlation. Second, based on the insights gained from our empirical study, we propose effective schemes to enhance two existing state-of-the-art demosaicking methods. Experimental results show that our enhanced methods achieve notable improvements over the existing methods, in terms of both subjective and objective evaluations, on a large variety of test images. In addition, the computational complexities of the enhanced methods are comparable to the originals.