Sparse signals can be recovered with less number of measurements compared to standard methods using Compressive Sensing (CS) theory. In digital cameras, color filter arrays (CFA) are used to sample each color band with less measurements than the normal. The color images are reconstructed using interpolation of measured pixel values. In this study, assuming images are sparse or compressible in a basis demosaicking is done with CS using the measurements from the CFA pattern. Separate, together and joint sparsity models are used for reconstructing images. Reconstructed sparsity levels for used CFA patterns are found. The images reconstructed with the proposed method are compared with the results from bilinear interpolation. © 2012 IEEE.