摘要
提出了一个能增强图像边缘的异性扩散模型,结合P-M扩散模型和反热扩散模型各自的优点,能在去除图像噪声的同时增强图像的边缘,一定程度上克服了P-M扩散模型对图像边缘的模糊效应和反热扩散模型容易产生虚假边缘的缺点。实验结果表明:提出的模型有很好的去噪和增强图像边缘的效果,其峰值信噪比(Peak Signal Noise Ratio,PSNR)在强噪声水平下,较P-M扩散模型大约提高1 dB。
This paper presents an anisotropic diffusion model which can enhance the edge of image.This model combines the P-M diffusion model and reverses thermal diffusion model.The model can remove the image noise together with en-hancing the edge of the image.It also prevents the blurring effect on the P-M diffusion model for the edge of image,and compared with reverse thermal diffusion modei,t overcomes the disadvantage of false edges’ appearance.The experimental re-sults show that the anisotropic diffusion model can greatly remove the noise and enhance the edge of image.Peak Signal Noise Ratio（PSNR）is 1 dB higher than that of P-M model,which is under strong noise.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第35期209-211,241,共4页
Computer Engineering and Applications
基金
重庆市科委资助项目(No.2008BB2356)
关键词
P-M扩散模型
反热扩散模型
图像去噪
图像增强
峰值信噪比
P-M diffusion model
reverse thermal diffusion modei
mage noise removali
mage enhance
Peak Signal Noise Ratio（PSNR）
