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.

► We proposed a strictly convex model to restore the multiplicative noisy image. ► We proposed a fast alternating algorithm to solve our proposed model. ► We gave the convergence analysis of our proposed algorithm. ► We applied our model and algorithm to related test images.