基于冗余Contourlet变换的图像相关法去噪

Contourlet变换是多尺度几何分析中十分重要的一种方法,可以实现灵活的多分辨、局部、多方向图像表示,但是由于不具有平移不变性,在图像去噪中易产生伪吉布斯现象,这里应用冗余Contourlet变换,具有平移不变性,且能有效表示图像几何纹理信息。在去噪应用中考虑分解系数的层间信息,将BivaShrink方法推广到冗余Contourlet变换中。实验结果表明,本文方法提高了去噪后图像的峰值信噪比(PSNR),同时有效保存了图像纹理信息,视觉效果更好。

The Image Denoising with Correlation Based on Redundant Contourlet Transform

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.