The Image Denoising with Correlation Based on Redundant Contourlet Transform
基于冗余Contourlet变换的图像相关法去噪

Contourlet transform(CT) is a method of multiscale geometric analysis,which can result in a flexible multi-resolution,local,and directional image expansion.But the Contourlet transform is not shift-invariant,that will cause pseudo-Gibbs phenomena around singularities in image denoising.In this paper we apply redundant contourlet transform with shift-invariant to image denosing,which can capture the intrinsic geometrical structure of image.Meanwhile,we consider the dependencies between the coefficients and their parents in detail.We propose a method of image denoising based on redundant contourlet with bivariate shrinkage rules.The experimental results show that our method can obtain higher PSNR value and better visual effect compared with other methods.