A Weberized Total Variation Regularization-Based Image Multiplicative Noise Removal Algorithm

A Weberized Total Variation Regularization-Based Image Multiplicative Noise Removal Algorithm

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Abstract Multiplicative noise removal is of momentous significance in coherent Gift Cards 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 Computer and design a novel way of fast alternating optimizing algorithm which contains three subminimizing parts and each 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.