光子学报, 2018, 47 (3): 0312001, 网络出版: 2018-02-01
基于经验小波变换及核概率密度的物体表面变形测量
Measurement of Objects Surface Deformation Using EWT and Kernel Probability Density
数字散斑干涉 相位提取 经验小波变换 表面变形 核概率密度估计 Digital speckle pattern interferometry Phase retrieval Empirical wavelet transform Surface deformation Kernel probability density
摘要
采用CCD相机采集物体变形前后的散斑图片, 利用一维经验小波变换对散斑图片进行逐行分解, 获得一系列的固有分量. 根据分解后分量的核概率密度函数提出基于核概率密度的自适应降噪法, 去除噪声干扰, 提取跟变形信息相关的分量并重构, 利用重构后的每一行获得变形前后重构散斑图. 采用Hilbert法计算重构后散斑图的相位, 对变形前后散斑图相位进行相减, 根据相位差进行解包裹获得物体表面变形信息.实验结果表明该方法能够有效地对物体表面变形进行测量, 且测量精度较经验模态分解提高4倍.
Abstract
In order to accurately measure the surface deformation information, a surface deformation measurement method based on Empirical Wavelet Transform (EWT) and kernel probability density was proposed. Firstly, the CCD camera was used to collect the digital speckle pattern interferometry (DSPI) maps, the DSPI maps were decomposed to obtain a series of intrinsic components by EWT. According to the kernel probability density of the decomposed component, an adaptive de-noising method was proposed to extract the components with the deformation information, the components were reconstructed DSPI maps; Finally, using Hilbert method to calculate reconstructed DSPI phase, the DSPI phase before and after deformation was subtracted, and the surface deformation information was obtained by decoupling according to the phase difference. The experimental results showed that the method can effectively measure the object surface deformation, and the accuracy is improved by 4 times compared with the EMD method.
肖启阳, 李健, 吴思进, 杨连祥, 董明利, 曾周末. 基于经验小波变换及核概率密度的物体表面变形测量[J]. 光子学报, 2018, 47(3): 0312001. XIAO Qi-yang, LI Jian, WU Si-jin, YANG Lian-xiang, DONG Ming-li, ZENG Zhou-mo. Measurement of Objects Surface Deformation Using EWT and Kernel Probability Density[J]. ACTA PHOTONICA SINICA, 2018, 47(3): 0312001.