In this paper, we address the problem of recovering a color image from a grayscale one. The input color data comes from a source image considered as a reference image. Reconstructing the missing color of a grayscale pixel is here viewed as the problem of automatically selecting the best color among a set of colors candidates while simultaneously ensuring the local spatial coherency of the reconstructed color information. To solve this problem, we propose a variational approach where a specific energy is designed to model the color selection and the spatial constraint problems simultaneously. The contributions of this paper are twofold: first, we introduce a variational formulation modeling the color selection problem under spatial constraints and propose a minimization scheme which allows computing a local minima of the defined non-convex energy. Second, we combine different patch-based features and distances in order to construct a consistent set of possible color candidates. This set is used as input data and our energy minimization allows to automatically select the best color to transfer for each pixel of the grayscale image. Finally, experiments illustrate the potentiality of our simple methodology and show that our results are very competitive with respect to the state-of-the-art methods.