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 Ribbed Turtleneck 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 Shimmer Glass Can existence and uniqueness of a minimizer for this variational model.

Moreover, we develop a linearized gradient method to solve the associated Euler-Lagrange equation via a fixed-point iteration.Our experimental results show that the proposed model has good performance.