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一种去除椒盐噪声的自适应极值中值滤波算法
引用本文:曾宪佑,黄佐华,周进朝,张勇.一种去除椒盐噪声的自适应极值中值滤波算法[J].计算机与现代化,2012(11):70-73,77.
作者姓名:曾宪佑  黄佐华  周进朝  张勇
作者单位:华南师范大学物理与电信工程学院量子信息技术实验室,广东广州510006
基金项目:广东省科技计划项目(C60109,2006B12901020)
摘    要:针对椒盐噪声的特点,为了更好地滤除图像中的椒盐噪声同时又能较好地保护图像细节,提出一种自适应极值中值滤波算法。该算法通过对窗口内的非噪声点的检测自适应调整窗口大小,使用Max-Min算子作为噪声检测器,通过设置合理的阈值对灰度值等于极大值或者极小值的窗口中心的像素点进行噪声识别,减小将信号点误判为噪声点的概率,然后将检测出的噪声点用窗口内信号点的中值代替,而信号点保持不变直接输出。同时对超过设定的最大窗口的情况,窗口中心的像素点的灰度值用4个相邻的已处理的像素点灰度值的均值进行替换。实验仿真结果证明了该算法滤除椒盐噪声的有效性,在噪声较大时,去噪效果更明显。

关 键 词:自适应极值中值滤波  噪声检测  阈值

An Adaptive Extremum and Median Filtering Algorithm for Salt and Pepper Noise
ZENG Xian-you,HUANG Zuo-hua,ZHOU Jin-zhao,ZHANG Yong.An Adaptive Extremum and Median Filtering Algorithm for Salt and Pepper Noise[J].Computer and Modernization,2012(11):70-73,77.
Authors:ZENG Xian-you  HUANG Zuo-hua  ZHOU Jin-zhao  ZHANG Yong
Affiliation:(Laboratory of Quantum Information Technology,School of Physics and Telecommunication Engineering, South China Normal University,Guangzhou 510006,China)
Abstract:In order to preserve more details in an image while removing salt and pepper noise,an adaptive extremum and median filtering algorithm is proposed,according to the feature of salt and pepper noise.The size of the filtering window is adjusted automatically by detecting the presence of signal points in the window.Max-Min operator is used as the noise detector and a suitable threshold is chosen to identify the noises from the pixels which are in the center of the window and the gray value is equal to the maximum gray value or the minimum gray value of the window.Then filter outs the detected noise with the median value of the gray value of all signal points in the window,and outputs the signal points directly without changes.The gray value of the pixels in the center of window which exceeds the maximum window is replaced with the mean of the gray value of neighboring four pixels.Simulation results demonstrate the validity of the algorithm,especially when the density is high.
Keywords:adaptive extremum and median filtering  noise detection  threshold
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