An adaptive segmentation-based regularization term for image restoration

This paper proposes an original inhomogeneous restoration (deconvolution) model under the Bayesian framework. In this model, regularization is achieved, during the iterative restoration process, with an adaptive segmentation-based regularization term whose goal is to apply local smoothness constraints on estimated constant areas of the image to be recovered. To this end, the parameters of this restoration a priori model relies on an unsupervised Markovian over-segmentation. To compute the MAP estimate associated to the restoration, we use a simple steepest descent procedure resulting in an efficient iterative process converging to a globally optimal restoration. The experiments reported in this paper demonstrate that the discussed method performs competitively and sometimes better than the best existing state-of-the-art methods in benchmark tests.