STABILIZED THRESHOLDING WITH GENERALIZED SURE FOR IMAGE DENOISING

We develop a novel stabilized thresholding methodology for image denoising. Main features of this new proposal include a new thresholding rule that repairs the known drawbacks of both hard and soft thresholding, and the use of the generalized Stein's unbiased risk estimation technique for automatic parameter selection. Another advantage of our approach is that it can be applied to different types of noise distributions. In this paper we consider both the Gaussian and mixture of Gaussians, where the latter case can be used to model data with outliers. Practical performance of our proposal is evaluated via numerical experiments.