计算机应用 ›› 2013, Vol. 33 ›› Issue (11): 3197-3200.

• 多媒体处理技术 • 上一篇    下一篇

基于城区距离的自适应加权均值滤波算法

曹萌1,2,张有会1,2,王志巍1,2,董蕊3,郑英娟1,2   

  1. 1. 河北师范大学 河北省计算数学与应用重点实验室,石家庄 050024;
    2. 河北师范大学 数学与信息科学学院,石家庄 050024;
    3. 河北交通职业技术学院 基础教学部,石家庄 050091
  • 收稿日期:2013-04-28 修回日期:2013-06-18 出版日期:2013-11-01 发布日期:2013-12-04
  • 通讯作者: 曹萌
  • 作者简介:曹萌(1989-),女,河北邢台人,硕士研究生,主要研究方向:图形图像处理;张有会(1955-),男,河北承德人,教授,主要研究方向:计算几何、图形图像处理;王志巍(1960-),男,辽宁锦州人,副教授,主要研究方向:算法设计、图像处理;董蕊(1979-),女,河北石家庄人,讲师,硕士,主要研究方向: 图形图像处理;郑英娟(1987-),女,河北邯郸人,硕士研究生,主要研究方向:图形图像处理。
  • 基金资助:
    国家自然科学基金资助项目;河北省科学技术研究与发展计划项目

Adaptive weighted mean filtering algorithm based on city block distance

CAO Meng1,2,ZHANG Youhui1,2,WANG Zhiwei1,2,DONG Rui3,ZHEN Yingjuan1,2   

  1. 1. Hebei Key Laboratory of Computational Mathematics and Applications, Hebei Normal University, Shijiazhuang Hebei 050024, China;
    2. Mathematics and Information Science College, Hebei Normal University, Shijiazhuang Hebei 050024, China;
    3. Foundamental Teaching Department, Hebei Jiaotong Vocational and Technical College, Shijiazhuang Hebei 050091, China
  • Received:2013-04-28 Revised:2013-06-18 Online:2013-12-04 Published:2013-11-01
  • Contact: CAO Meng

摘要: 针对传统滤波窗口不能自适应扩展以及标准均值滤波易造成图像边缘模糊的缺陷,提出一种基于城区距离的自适应加权均值滤波算法。首先,利用开关滤波思想检测出噪声点;其次,对于每一噪声点,依据城区距离扩展窗口,窗口的大小根据窗口内信号点的个数自适应地调节;最后,将窗口内足够数量信号点的灰度的加权平均值作为噪声点的灰度值,实现对噪声点的有效恢复。实验结果表明,该算法能够有效地滤除椒盐噪声,尤其对噪声密度较大的图像,去噪效果更加显著。

关键词: 城区距离, 自适应, 均值滤波, 高密度噪声, 图像去噪

Abstract: Concerning the defect that the traditional filtering window cannot be adaptively extended and the standard mean filter algorithm could blur edges easily, a new adaptive weighted mean filtering algorithm based on city block distance was proposed. First, the noise points can be detected with switch filtering ideas. Then, for each noise point, the window was extended according to the city block distance, and the window size was adaptively adjusted based on the number of signal points within the window. Last, the weighted mean of the signal points in the window was taken as the gray value of the noise points to achieve the effective recovery of the noise points. The experimental results show that the algorithm can effectively filter out salt-and-pepper noise, especially for the larger-noise-density image, and denoising effect is more significant.

Key words: city block distance, adaptive, mean filtering, high-density noise, image denoising

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