基于非局部均值的混沌映射噪声抑制算法

Noise Suppression Algorithm for Chaotic Mapping Based on Nonlocal Mean

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.

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.