计算机应用 ›› 2012, Vol. 32 ›› Issue (10): 2790-2792.DOI: 10.3724/SP.J.1087.2012.02790

• 图形图像处理 • 上一篇    下一篇

基于方向中值的图像椒盐噪声检测算法

陈健1,2,郑绍华2   

  1. 1. 福建工程学院 电子信息与电气工程系,福州 350108
    2. 福州大学 数字媒体研究院,福州 350002
  • 收稿日期:2012-04-27 修回日期:2012-06-07 发布日期:2012-10-23 出版日期:2012-10-01
  • 通讯作者: 陈健
  • 作者简介:陈健(1981-),男,福建古田人,讲师,博士研究生,主要研究方向:图像处理、多媒体通信、数字监控;郑绍华(1976-),男,福建大田人,讲师,博士研究生,主要研究方向:图像处理、多媒体通信。
  • 基金资助:
    国家自然科学基金资助项目

Salt and pepper noise detection algorithm based on directional median

CHEN Jian1,2,ZHENG Shao-hua2   

  1. 1. Department of Electronic Information and Electrical Engineering, Fujian University of Technology, Fuzhou Fujian 350108, China
    2. Research Academy of Digital Media, Fuzhou University, Fuzhou Fujian 350002, China
  • Received:2012-04-27 Revised:2012-06-07 Online:2012-10-23 Published:2012-10-01
  • Contact: CHEN Jian

摘要: 为了在有效去除椒盐噪声的同时最大限度地保持图像的细节,针对现有应用于椒盐噪声检测算法的优缺点,提出一种基于方向中值的椒盐噪声两级检测算法。算法通过初级全局噪声检测将图像分为可疑噪声点与信号点,二级检测中算法以可疑噪声点为中心在5×5的检测窗口中设置9个方向检测区,通过可疑噪声点灰度值与检测区像素点灰度中值的比较最终确定噪声点的位置。算法中的可行性漏检在保证图像质量的同时减少了后续处理的像素数,同时,算法具有较低的噪声误检率,保持了图像的细节。仿真实验结果验证了算法的有效性。

关键词: 图像去噪, 椒盐噪声, 噪声检测, 中值滤波, 检测窗口

Abstract: To remove the salt and pepper noise in images effectively while preserving the detail, a salt and pepper noise detection algorithm based on directional median was proposed after analyzing merits and drawbacks of different slat and pepper noise detection algorithms. The algorithm divided the image into doubtful noise pixels and signal pixels in the first step. At the second step, the algorithm set nine directional detection areas in the 5×5 detection window centered on the doubtful pixels, and confirmed the noise pixels after comparing gray value of doubtful noise pixels and median gray value of the detection areas. The feasibility of the leak detection in the algorithm reduced load of subsequent processing. At the same time, the algorithm possessed low false detection rate which preserved the detail. The simulation results show the validity of the algorithm.

Key words: image denoising, salt and pepper noise, noise detection, median filter, detection window

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