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We present an efficient linear minimum mean square error (LMMSE) method for reconstructing full color images from single sensor color filter array (CFA) data. We use a representative set of full color images to estimate the joint spatial-chromatic covariance among pixel color components. Then, we derive from it a set of joint color-space, small linear kernels which predict the missing color samples as linear combinations of their neighbor observed samples. The color arrangement of the local mosaic varies with the window's location, and this results into a different predictor for every local mosaic and color sample. As an extension, we include blur and noise in the training process, obtaining localized mosaic-constrained Wiener estimators that partially compensate for these degradations. We show that this simple method provides an excellent trade-off between performance and computational cost.