摘要
自适应中值滤波算法能有效地滤除图像的脉冲噪声,但是,随着噪声密度的增大,算法的滤波性能递减.当前对中值滤波算法进行改进的算法,也存在着相应的局限性.针对中值滤波算法的局限性,提出了改进的自适应中值滤波算法.算法根据滤波窗口的灰度极值进行噪声检测.对噪声点,用滤波窗口的灰度中值代替.如果中值为噪声点,则自适应地增大滤波窗口以取新的中值.如果窗口增大到允许的最大尺寸时,中值依然为噪声点,则取滤波窗口中除灰度极值外的其他像素的灰度均值.对标准图像和医学图像进行仿真实验,实验结果和数据证明,随着噪声密度的增大,标准的自适应中值滤波算法的滤波性能递减;改进的自适应中值滤波算法的滤波性能依然良好,在有效滤除噪声的同时,很好地保持图像的边缘和细节部分.
The adaptive median filtering algorithm can effectively filter the impulse noise of image, however, with the noise density increasing, its filtering performance decreases progressively. For the improved median filtering algorithms of current, there are also relevant limitations. Against the limitations of the median filtering algorithm, an improved adaptive median filtering algorithm is proposed. It does noise detection based on the gray extremum of the filtering window. And it replaces the noise point with the gray median of the filtering window. If the gray median is noise point, it increases adaptively the filtering window to take a new gray median. If the filtering window has increased to the maximum size of allowed, and the gray median is still noise point, it takes the gray mean of the pixels except the gray extremum in the filtering window. Simulation experiment has been carried out for standard image and medical image, the results and datum of the filtering experiment demonstrate that, with the noise density increasing, the filtering performance of the standard adaptive median filtering algorithm decreases progressively; and the filtering performance of improved adaptive median filtering algorithm is still good, it maintains well the edges and details of image while filtering effectively the noise.
作者
黄文笔
战荫伟
陈家益
徐秋燕
HUANG Wen-Bi;ZHAN Yin-Wei;CHEN Jia-Yi;XU Qiu-Yan(Center of Educational Technology and Information,Guangdong Medical University,Dongguan 523808,Chin;School of Computer Science and Technology,Guangdong University of Technology,Guangzhou 510006,Chin;School of Information Engineering,Guangdong Medical University,Zhanjiang 524023,China;Surgical ICU,Central People＇s Hospital of Zhanjiang,Zhanjiang 524045,China)
出处
《计算机系统应用》
2018年第10期183-188,共6页
Computer Systems & Applications
基金
国家自然科学基金(61170320
11347150)
广东省自然科学基金(2015A030310178
2014A030310239)
广州市科技计划项目(201604016034)
广东省医学科研基金(B2018190)
湛江市科技攻关计划项目(2017B01142)
广东医科大学科研基金(M2016046)~~
关键词
噪声检测
滤波窗口
灰度极值
中值滤波算法
noise detection
filtering window
gray extremum
median filtering algorithm
