A new efficient variational model for multiplicative noise removal
Lufeng Bai, Fang Liu, Shenyang Tan · International Journal of Computer Mathematics · 2019
In this paper, based on the approximation of the Taylor expansion, a novel fidelity term is formulated. We propose a new variational image restoration model to recover an image from its multiplicative noised version. We show that the proposed model is strictly convex and has a unique solution so that the model can be performed by the primal–dual method. Compared with other models, the proposed model is able to not only deal with various problems of different type noises, such as the multiplicative Beta noise, F noise, Gaussian noise, but also take significantly less computational time.