An Exp Model with Spatially Adaptive Regularization Parameters for Multiplicative Noise Removal

This article proposes a total variation (TV) based model with local constraints for heavy multiplicative noise removal. The local constraint involves multiple local windows rather than one local window as in Chen and Cheng (IEEE Trans Image Process 21(4):1650–1662, 2012), and the proposed model is an extension model of Lu et al. (Appl Comput Harmon Anal 41(2):518–539, 2016) that incorporates a spatially adaptive regularization parameter, which enables us to handle heavy multiplicative noise as well as to sufficiently denoise in homogeneous regions while preserving small details and edges. In addition, convergence analysis such as the existence and uniqueness of a solution for our model is also provided. We also derive an optimization algorithm from the first-order optimality characterization of our model. Furthermore, we utilize a proximal linearized alternating direction algorithm for efficiently solving our subproblem. Numerical results are shown to validate the effectiveness of our model, with comparisons with several existing TV based models.