Image denoising by non-subsampled shearlet domain multivariate model and its method noise thresholding

Abstract

The denoising of a natural image corrupted by additive Gaussian white noise is a classical problem in image processing. A new image denoising method is proposed by using the dependencies between the non-subsampled shearlet transform (NSST) coefficients and their neighbors. The NSST is well known for its approximate shift invariance and better directional selectivity, which are very important in image denoising. In order to take account of the dependency between the current NSST coefficient and its neighbors, a new multivariate probability density function (pdf) is exploited, and a multivariate shrinkage function for image denoising is derived from it by using the maximum a posteriori (MAP) estimator. Then, the blend of the proposed multivariate shrinkage function and its method noise thresholding using NSST is proposed for image denoising. Experimental results demonstrate that the proposed method is very competitive when compared with other existing denoising methods in the literature.