Variational Image Restoration with Constraints on Noise Whiteness

We propose a novel variational framework for image restoration based on the assumption that noise is additive and white. In particular, the proposed variational model uses total variation regularization and forces the resemblance of the residue image to a white noise realization by imposing constraints in the frequency domain. The whiteness constraint constitutes the key novelty behind our approach. The restored image is efficiently computed by the constrained minimization of an energy functional using an alternating directions methods of multipliers procedure. Numerical examples show that the novel approach is particularly suited for textured image restorations.