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Gauss-Markov random field (GMRF) image models are commonly used in many Bayesian-based imaging techniques to define priors that lead to computationally tractable solutions for the image restoration problem. Recently, the color reconstruction literature has demonstrated, and effectively employed, the high correlation among the bands of a color image for color reconstruction. In this paper, we formulate a compound GMRF prior based on cross-channel spatial derivatives that reflects the smoothness in the color-difference space in addition to the often used intra-channel smoothness assumption. The proposed model is used to develop an effective method for restoring sparsely sampled color images in the presence of noise. The value of the proposed method is demonstrated on the problem of color reconstruction for single-sensor cameras.