Iteratively reweighted second-order regularization based multiplicative noise removal model
Wang Xu-don · Journal of Xidian University · 2014
In order to remove the multiplicative noise in an image,an iteratively reweighted second order derivatives(Frobenius norm of Hessian matrix)regularization model is proposed under the assumption that the multiplicative noise follows a Gamma distribution,which is an extension of the iteratively reweighted total variation model.A primal-dual algorithm for iteratively reweighted Frobenius norm of the Hessian matrix regularization model is designed.Numerical experiments show that the proposed model and algorithm can remove noise effectively while preserving details,restraining the staircase effect and avoiding edge blurring.