A Nonlinear Inverse Scale Space Method for Multiplicative Noise Removal Based on Weberized Total Variation

Multiplicative noise removal has been drawn a greatly attention recently. Firstly, this paper proposes a new non-convex variational model for multiplicative noise removal under the Weberized TV regularization framework. Then we propose and study another surrogate strictly convex objective functional for Weberized TV regularization based multiplicative noise removal model. Finally, we adopt the recently proposed inverse scale space approach to estimate the underlying image under total variation (TV) regularization, in which a relaxation technique with two evolution equations is applied. Our experimental results show that the quality of images denoised is quite good and the detail information of restored images is well preserved by the proposed algorithm.