An Efficient Variational Model for Restoring Noisy Images with Gamma Multiplicative Noise

Abstract

Multiplicative noise removal has been a focus of research in recent years. Aiming at solving the problem that the total variation regularization method can remove noise well but sometimes produce stair-case effect, this paper proposes an efficient variational model and gives the iterative algorithm to remove Gamma multiplicative noise. This paper also shows that the iterative sequence converges to the optimal solution of the model. Through simulation experiments the proposed model has proved highly effective. That is, this model can preserve the edges of the image well and significantly reduce the stair-case effect in the smooth regions while removing Gamma multiplicative noise effectively.