Abstract Multiplicative noise removal is of momentous significance sensationnel kiyari in coherent imaging systems and various image processing applications.This paper proposes a new nonconvex variational model for multiplicative noise removal under the Weberized total variation (TV) regularization framework.Then, we propose and investigate another surrogate strictly convex objective function for Weberized TV regularization-based multiplicative noise removal model.Finally, we propose and design a novel way of fast alternating optimizing algorithm which contains three subminimizing parts and each ww1 german helmet decals of them permits a closed-form solution.
Our experimental results show that our algorithm is effective and efficient to filter out multiplicative noise while well preserving the feature details.