Iteratively Reweighted Anisotropic-TV Based Multiplicative Noise Removal Model
去除乘性噪声的重加权各向异性全变差模型

Multiplicative noise removal is an important research topic on image processing. This paper proposes an iteratively reweighted anisotropic-total variation (TV) based model under the assumption that the multiplicative noise follows a Gamma distribution. The regularization term is the weighted anisotropic-TV regularizer. The weighting function incorporated in the regularization term is derived from the expectation maximization (EM) algorithm. The merits of this model are the preservation of geometrical structures of edges and the restraint of "staircase effect" while removing the noise. Numerical experimental results demonstrate the better performance of the proposed model.