一种去除椒盐噪声的自适应模糊中值滤波算法

TP391; 针对传统中值滤波算法降噪性能不佳以及易造成图像细节模糊的问题,提出了一种自适应模糊中值滤波算法.滤波过程分为噪声检测和噪声去除2个阶段.噪声检测阶段:采用极值法检测噪声,将图像的像素点分为两类,即疑似噪声点和信号点.通过疑似像素点和与其相邻的已处理的像素点的平均绝对灰度差值定义模糊隶属度函数,利用该函数对疑似噪声点是否为噪声进行模糊分类.噪声去除阶段:信号点保持原值输出,对于疑似噪声点的3种分类结果,采用模糊加权的中值滤波器进行统一处理.实验结果表明,较于多种传统滤波方法,该算法能更有效地去除椒盐噪声,保护图像细节....

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Published in浙江大学学报（理学版） Vol. 46; no. 4; pp. 445 - 453
Main Authors 万丰丰, 周国民, 周晓
Format Journal Article
LanguageChinese
Published 浙江工业大学 信息工程学院,浙江 杭州,310023%浙江警察学院,浙江 杭州,310053 01.07.2019
Subjects
中值滤波
自适应模糊中值滤波
图像降噪
椒盐噪声
Online AccessGet full text
ISSN1008-9497
DOI10.3785/j.issn.1008-9497.2019.04.010

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Abstract TP391; 针对传统中值滤波算法降噪性能不佳以及易造成图像细节模糊的问题,提出了一种自适应模糊中值滤波算法.滤波过程分为噪声检测和噪声去除2个阶段.噪声检测阶段:采用极值法检测噪声,将图像的像素点分为两类,即疑似噪声点和信号点.通过疑似像素点和与其相邻的已处理的像素点的平均绝对灰度差值定义模糊隶属度函数,利用该函数对疑似噪声点是否为噪声进行模糊分类.噪声去除阶段:信号点保持原值输出,对于疑似噪声点的3种分类结果,采用模糊加权的中值滤波器进行统一处理.实验结果表明,较于多种传统滤波方法,该算法能更有效地去除椒盐噪声,保护图像细节.
AbstractList TP391; 针对传统中值滤波算法降噪性能不佳以及易造成图像细节模糊的问题,提出了一种自适应模糊中值滤波算法.滤波过程分为噪声检测和噪声去除2个阶段.噪声检测阶段:采用极值法检测噪声,将图像的像素点分为两类,即疑似噪声点和信号点.通过疑似像素点和与其相邻的已处理的像素点的平均绝对灰度差值定义模糊隶属度函数,利用该函数对疑似噪声点是否为噪声进行模糊分类.噪声去除阶段:信号点保持原值输出,对于疑似噪声点的3种分类结果,采用模糊加权的中值滤波器进行统一处理.实验结果表明,较于多种传统滤波方法,该算法能更有效地去除椒盐噪声,保护图像细节.
Author 周晓
万丰丰
周国民
AuthorAffiliation 浙江工业大学 信息工程学院,浙江 杭州,310023%浙江警察学院,浙江 杭州,310053
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ZHOU Xiao
WAN Fengfeng
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Keywords 中值滤波
自适应模糊中值滤波
图像降噪
椒盐噪声
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