基于灰色关联度改进的Contourlet变换图像去噪算法

为了更有效降低图像中的噪声，提出一种基于灰色关联度改进的Contourlet变换图像去噪算法。一方面考虑到Contourlet变换尺度内各相邻方向子带之间的灰色关联度、尺度间的影响及噪声强度的因素，对贝叶斯阈值进行改进;另一方面根据Contourlet系数的特点对折中阈值函数进行改进，以达到自适应去噪的目的。实验结果表明，该算法能有效地降低图像噪声，获得更高的峰值信噪比(PSNR)和更好的视觉效果，具有较好的实用性。

Improved image denoising algorithm of Contourlet transform based on gray relational degree

In order to denoise image more effectively, an improved Contourlet transform denoising algorithm based on gray relational degree was proposed. On one hand, considering the gray relational degree and inter-scale from the high-frequency sub-band and low frequency sub-band by Contourlet transform, the Bayes threshold was improved; On the other hand, in order to achieve the purpose of adaptive denoising, the characteristics of Contourlet coefficients were used to improve the compromising threshold function. The experimental results show that the proposed algorithm can denoise image effectively, get higher PSNR and better visual quality, and has a good practicability.