Wang Hanxiao, Miao Yinghao, Yang Hailu, Ye Zhoujing, Wang Linbing
Appl Opt. 2020 Jul 10;59(20):6191-6202. doi: 10.1364/AO.395603.
The uneven background illumination and random noise will degrade the quality of the optical fringe pattern, resulting in reduced accuracy or errors in phase extraction of wavelet transform profilometry (WTP). An adaptive fringe pattern enhancement method is proposed in this paper, which can effectively solve the above problems and improve the robustness of WTP. First, a modified intrinsic time-scale decomposition (MITD) algorithm is used to decompose each row of the fringe pattern adaptively, which can obtain a set of reasonable and pure proper rotation components (PRCs) with a frequency ranging from high to low and a monotonic trend. The MITD algorithm can overcome the mode mixing problem while ensuring the completeness of decomposition. Then, based on the obtained pure PRCs, an innovative background-carrier signal-noise automatic grouping strategy is proposed. Specifically, weighted-permutation entropy (WPE) is adopted to handle noise removal, and fuzzy gray correlation analysis (FGCA) is used to separate the background and carrier signal. Finally, the desired phase information can be easily and accurately extracted from the enhanced carrier signal component by a direct wavelet ridge detection method. Both the simulation and experimental results demonstrate the effectiveness and functionality of the proposed method.
不均匀的背景照明和随机噪声会降低光学条纹图案的质量，导致小波变换轮廓术（WTP）的相位提取精度降低或出现误差。本文提出了一种自适应条纹图案增强方法，该方法能够有效解决上述问题并提高WTP的鲁棒性。首先，采用改进的固有时间尺度分解（MITD）算法对条纹图案的每一行进行自适应分解，可获得一组合理且纯净的具有从高到低频率和单调趋势的适当旋转分量（PRC）。MITD算法在确保分解完整性的同时能够克服模态混叠问题。然后，基于所获得的纯净PRC，提出了一种创新的背景-载波信号-噪声自动分组策略。具体而言，采用加权排列熵（WPE）进行噪声去除，利用模糊灰色关联分析（FGCA）分离背景和载波信号。最后，通过直接小波脊线检测方法可以轻松、准确地从增强后的载波信号分量中提取所需的相位信息。仿真和实验结果均证明了所提方法的有效性和实用性。
