基于改进PDE扩散系数的图像去噪研究

Research on Image Denoising by Improving the Diffusion Coefficient of PDE

基于偏微分方程的图像处理方法是近年来比较热门的一种图像处理方法,因为偏微分方程具有各向异性特点,使其在对图像去噪的同时又能够较好地保护图像的边缘。文章主要在前人研究的基础上,对P-M模型扩散系数进行改进。文章讨论了扩散系数对图像去噪的影响,并提出一种新的扩散函数,结合正则化的P-M模型,进行数值离散实验。实验结果表明,改进的扩散函数能够很好地对图像去噪,在迭代次数、运行时间、信噪比和峰值信噪比等方面都要优于P-M方程中所给的扩散函数。

The image processing method of partial differential equations(PDE)is a popular method in recent years.The anisotropy of PDE can play an important role in image denoising and protect the edge of the image at the same time.In this paper,the diffusion coefficient of P-M model is improved in order to get better result.The paper is organized as follows.Firstly,the effect of diffusion coefficient on image denoising is discussed,and a new diffusion function is proposed,which is combined with regularized P-M model to carry out numerical discretization experiments.The experimental results show the effectiveness of the method in this paper.The number of iterations,the running time,the signal-to-noise ratio and the peak signal-to-noise ratio are better than two diffusion functions of P-M equation.