Multiplicative Noise Removal via a Novel Variational Model

Multiplicative Noise Removal via a Novel Variational Model

Blog Article

Multiplicative noise appears in various image processing applications, such as synthetic aperture radar, ultrasound imaging, single particle emission-computed tomography, and positron emission tomography.Hence multiplicative noise removal is Brush of momentous significance in coherent imaging systems and various image processing applications.This paper proposes a nonconvex Bayesian type variational model for multiplicative noise removal which includes the total variation (TV) and the Weberized TV as regularizer.We study the issues of existence and uniqueness of a minimizer for this variational model.Moreover, we develop a linearized gradient method to solve the associated Euler-Lagrange Tools equation via a fixed-point iteration.

Our experimental results show that the proposed model has good performance.