摘要
为减少表面功能评定中测量数据采样起始点位置对传统小波模型滤波结果的影响,提出了一种基于双密度小波变换的表面形貌分离新方法。将原始轮廓信号通过双密度小波分解为一系列小波函数和尺度函数的线性叠加,对表面各不同成分所对应的小波系数进行重构,即可得到所需的表面形貌的分离信号。仿真和实验验证表明:提出方法得到的分离粗糙度、波度等频率成分的准确性比其它方法提高了4%左右。新方法减小了采样点位置对滤波结果的影响,可以实现对表面特征平移不变的有效分离和提取,并提高了表面测量的精度。
A novel signal separation method based on Double Density Wavelet Transform （DDWT） was presented to reduce the influence of the locations of sampling points on the filtering results from traditional wavelet in surface evaluation. The original profile signal could be decomposed by DDWT into a linear superposition of wavelet functions and scaling functions. After reconstruction of wavelet coefficients corresponding to different components, the required surface topographic discrete signal was obtained. The experiment results show that the separation accuracy of roughness, waveness and other frequency components can be higher about 4% than that of other methods. In engineering surface analysis, the new method can provide near shift-invariance in surface texture separation and extraction,and can decrease the influence of sampling points on filtering results and also improve the measurement precision.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2008年第6期1093-1097,共5页
Optics and Precision Engineering
基金
黑龙江省科技攻关基金资助项目(No.2006G0780-11)
关键词
小波分析
表面形貌
双密度小波
表面评定
wavelet analysis
surface topography
double density wavelet
surface evaluation
