Denoising Method Based on the Nonsubsampled Shearlet Transform

摘要

In this paper, a new bivariate shrinkage denoising method is proposed to model statistics of shearlet coefficients of images. Using Bayesian estimation theory we derive from this model a simple non-linear shrinkage function for shearlet denoising, which generalizes the soft threshold approach. Experimental results show that the proposed method can remove Gaussian white noise while effectively preserving edges and texture information. At the same time, it can achieve a higher PSNR and mean structural similarity than other denoising method.