华南理工大学学报(自然科学版) ›› 2015, Vol. 43 ›› Issue (5): 40-44,50.doi: 10.3969/j.issn.1000-565X.2015.05.007

• 电子、通信与自动控制 • 上一篇    下一篇

基于非局部均值的混沌映射噪声抑制算法

王梦蛟 冯久超 吴中堂 方杰 王前   

  1. 华南理工大学 电子与信息学院,广东 广州 510640
  • 收稿日期:2014-12-08 修回日期:2015-01-12 出版日期:2015-05-25 发布日期:2015-05-07
  • 通信作者: 冯久超(1964-),男,教授,博士生导师,主要从事电路及信号处理的理论与应用研究. E-mail:fengjc@scut.edu.cn
  • 作者简介:王梦蛟(1983-),男,博士生,讲师,主要从事混沌信号处理研究. E-mail: wangmengjiao_1983@163. com
  • 基金资助:

    国家自然科学基金资助项目(60872123);NSFC-广东省自然科学联合基金资助项目(U0835001);华南理工大学中央高校基本科研业务费专项资金资助项目(2013ZM0080)

Noise Suppression Algorithm for Chaotic Mapping Based on Nonlocal Mean

Wang Meng-jiao Feng Jiu-chao Wu Zhong-tang Fang Jie Wang Qian   

  1. School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
  • Received:2014-12-08 Revised:2015-01-12 Online:2015-05-25 Published:2015-05-07
  • Contact: 冯久超(1964-),男,教授,博士生导师,主要从事电路及信号处理的理论与应用研究. E-mail:fengjc@scut.edu.cn
  • About author:王梦蛟(1983-),男,博士生,讲师,主要从事混沌信号处理研究. E-mail: wangmengjiao_1983@163. com
  • Supported by:
    Supported by the National Natural Science Foundation of China(60872123),the Joint Fund of the National Natural Science Foundation and the Guandong Provincial Natural Science Foundation(U0835001)

摘要: 提出了一种基于非局部均值的混沌映射噪声抑制算法. 该算法根据混沌映射的特征,利用实验分析得出非局部均值应用于混沌映射噪声抑制时滤波参数块长、搜索区间和带宽参数的最优取值. 仿真结果表明,文中算法对高斯噪声的抑制性能优于现有的相空间估计投影方法、扩展卡尔曼滤波方法和无先导卡尔曼滤波方法,能对不同噪声水平的混沌映射进行有效的噪声抑制.

关键词: 混沌映射, 信号噪声抑制, 非局部均值, 自适应滤波

Abstract: Proposed in this paper is a noise suppression algorithm for chaotic mapping on the basis of nonlocal mean. According to the characteristics of chaotic mappings,this algorithm obtains the optimal filtering parameters (including patch size,search neighborhood and bandwidth) of the nonlocal mean applied to the noise suppression of chaotic mappings via experimental analysis. Simulated results show that the proposed algorithm outperforms such existing methods as phase space estimating projection,extended Kalman filtering and unscented Kalman filtering in terms of Gaussian noise suppression; and that it effectively suppresses chaotic mappings at different noise levels.

Key words: chaotic mapping, signal noise suppression, nonlocal mean, adaptive filtering