A novel non-subsampled Contourlet transform denoising scheme based on the normal inverse Gaussian prior (NIG) and Bayesian estimation has been proposed.Normal inverse Gaussian model is used to describe the distributions of the image coefficients of each subband in non-subsampled Contourlet transform domain

corresponding threshold function is derived from the model using Bayesian maximum a posteriori probability estimation theory.This scheme achieves enhanced estimation results for images that are corrupted with additive Gaussian noise over a wide range of noise variance.The simulation results indicate that the proposed method can remove Gaussian white noise effectively

improve the peak signal-to-noise ratio of the image

and keep better visual result in edges information reservation as well.