Bayes理论和邻域平均法在图像去噪中的应用  

Application of Bayes decision and neighborhood averaging method on image denoising

作　　者：杨会云[1] 张有会[1] 霍利岭[1] 赵金[1] 

机构地区：[1]河北师范大学数学与信息科学学院,石家庄050016

出　　处：《计算机工程与应用》2010年第9期149-151,共3页Computer Engineering and Applications

摘　　要：去噪处理是图像处理中较为重要的环节。针对加噪后的图像的直方图进行分析,依据最小错误率贝叶斯决策和均值滤波理论,提出一种基于均值滤波和最小错误率贝叶斯决策的去噪方法。首先对加入噪声后的图像直方图进行统计,从中估计出服从分布的不同类别参数,对图像中每一像素点进行判断是否为噪声,对噪声点进行基于均值滤波的处理。通过试验,取得了良好的效果。Noise reduction is one of the most important parts of image processing.This paper analyzes the histogram of noise polluted images and presents a novel denoising algorithm based on the minimum error Bayes decision and the theory of meaning filtering.First the histogram of noise polluted images is counted and difference parameters are estimated.Then whether it is a noise or not to each pixel point in the image is decided.And the noise using meaning filtering is processed.The experiment results show that the algorithm presented in this paper is feasible and good.

关 键 词：图像去噪 贝叶斯决策 均值滤波 直方图 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]