Low-complexity linear demosaicing using joint spatial-chromatic image statistics

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.