Edge-Preserving Image Smoothing Via a Total Variation Regularizer and a Nonconvex Regularizer

Image smooth plays an important role in image pre-processing. For classical image smoothing models, a convex total variation regularizer or a nonconvex regularizer has been widely used to protect image edges and to smooth noise. In this paper, we propose a new effective model in which the total variation regularizer and the nonconvex regularizer are combined to be a new weighted regularizer. The main advantage of our combined regularizer is that some undesirable details such as noise can be removed more effectively, while some important edge details can be preserved better. In addition, an efficient algorithm is designed to solve our model. In our algorithm, an iteratively reweighted process with the Chambolle's projection algorithm are coupled with each other. Numerical results demonstrate that our proposed model can generate better image smoothing results than those generated by total variation based models and those generated by nonconvex regularizer based models. (C) 2019 The Authors. Published by Elsevier Ltd.