Fast algorithm for multiplicative noise removal
In this work, we consider a variational restoration model for multiplicative noise removal problem. By using a maximum a posteriori estimator, we propose a strictly convex objective functional whose minimizer corresponds to the denoised image we want to recover. We incorporate the anisotropic total variation regularization in the objective functional in order to preserve the edges well. A fast alternating minimization algorithm is established to find the minimizer of the objective functional efficiently. We also give the convergence of this minimization algorithm. A broad range of numerical results are given to prove the effectiveness of our proposed model. Crown Copyright (C) 2011 Published by Elsevier Inc. All rights reserved.
multiplicative noise maximum a posteriori anisotropic total variation alternating minimization algorithm convex function proximal operator gamma distribution newton method