Demosaicing of noisy color images through edge-preserving regularization

We propose edge-preserving regularization for color image demosaicing in the realistic case of noisy data. We enforce both intrachannel local smoothness of the intensity, and interchannel local similarities of the edges. To describe these local correlations while preserving even the finest image details, we exploit suitable functions of the derivatives of first, second and third order. The solution of the demosaicing problem is defined as the minimizer of a non-convex energy function, accounting for all these constraints plus a data fidelity term. Minimization is performed via an iterative deterministic algorithm, applied to a family of approximating functions, each implicitly referring to meaningful discontinuities. Our method is irrespective of the specific color filter array employed. However, to permit quantitative comparisons with other published results, we tested it in the case of the Bayer CFA, and on the Kodak 24-image set.