In this paper, we introduce a novel approach in the nonconvex optimization framework for image restoration via a Markov random field (MRF) model While image restoration is elegantly expressed in the language of MRF’s, the resulting energy minimization problem was widely viewed as intractable it exhibits a highly nonsmooth nonconvex energy function with many local minima and is known to be NP-hard. The main goal of this paper is to develop fast and scalable approximation optimization approaches to a nonsmooth nonconvex MRF model which corresponds to an MRF with a truncated quadratic (also known as halfquadratic) prior. For this aim, we use the difference of convex functions (DC) programming and DC algorithm (DCA) a fast and robust approach in smooth nonsmooth nonconvex programming, which have been successfully applied in
various fields in recent years. We propose two DC formulations and investigate the two corresponding versions of DCA. Numerical simulations show the efficiency reliability and robustness of our customized DCAs with respect to the standard GNC algorithm and the Graph-Cut based method—a more recent and efficient approach to image analysis.

EB00000003345K

Available