基于GANs网络的条纹图正交化方法  

Fringe Pattern Orthogonalization Method by Generative Adversarial Nets

作　　者：冯雷洁 杜虎兵[1] 张高鹏[2] 李燕杰 韩金璐 FENG Leijie;DU Hubing;ZHANG Gaopeng;LI Yanjie;HAN Jinlu(School of Mechatronic Engineering,Xi'an Technological University,Xi'an 710021;Xi'an Institute of Optics and Precision Mechanics,Chinese Academy of Sciences,Xi'an 710119)

机构地区：[1]西安工业大学机电工程学院,西安710021 [2]中国科学院西安光学精密机械研究所,西安710119

出　　处：《光子学报》2023年第1期149-161,共13页Acta Photonica Sinica

基　　金：国家自然科学基金(No.51975448);陕西省重点研发计划(No.2021GY-274);中国科学院青年创新促进会会员项目(No.2022410)。

摘　　要：提出一种利用深度学习强大的隐式建模能力解决单帧条纹图正交化存在的欠采样问题,结合条纹图降噪归一化技术,利用对抗生成网络的特征先验,构造了一种条纹图轮廓项数字方式的π/2相移网路,实现了单帧条纹图的正交化,放松了应用解析模型法实现条纹图正交化时的严格要求。通过标签图像对训练后,该网络成功地实现了归一化后的条纹图的正交化,进而高精度地实现了单帧条纹图的相位解调。仿真和实验分析证明,与基于Riesz变换的数字相移方法相比,所提方法求解更可靠,能有效地恢复测量相位。以现有的多帧高精度相移算法的解调结果作为参考值,实验结果表明所提方法的相位误差分布在0.05 rad以内,为瞬变场和物体三维轮廓测量提供了一种途径。Optical measurement techniques, such as interferometry, moiré techniques, and digital holography, are the most popular noncontact approaches for measuring three-dimensional(3D) object surfaces in terms of non-invasive, fast, and accurate evaluation. Usually, the property of the measured quantity is encoded in the phase of the intensity distribution of the fringe pattern, which can be decoded by phase retrieval, in other words, the recovery of a complex-valued signal from the sampled intensity patterns. In this way, phase demodulation of the fringe pattern plays a crucial role in the ubiquitous optical measurements. Among various single frame phase demodulation techniques, the high-frequency fringe pattern demodulation technique, such as Fourier transform profilometry, sampling moiré method and spatial carrier phase-shifting have been intensively studied and are mainly based on known analytical models of measurement systems, such as harmonic representation of the intensity of fringe patterns. But for low-frequency fringe pattern, phase reconstruction from only a single interferogram is difficult, especially for those including closed fringes. Sign ambiguity during the single-frame demodulation is one of the main problems that impede the development of single-frame interferometry. In this case, fringe pattern orthogonalization plays a very important role in low-frequency fringe pattern phase extraction. However, due to the ill-posed problem of orthogonalization of a single frame fringe pattern, the development of an analytical method for fringe pattern orthogonalizing is full of challenges. In recent years, researchers have demonstrated that deep learning is a powerful machine learning technique that uses artificial neural networks with deep layers to fit complex mathematical functions, thereby, deep learning provides a promising improvement over classical methods derived from explicit analytical formulations of the forward models.More specifically, deep learning approaches handle problems by searching and establ

关 键 词：条纹图分析 相位解调 条纹图正交化 深度学习 三维轮廓测量 

分 类 号：O439[机械工程—光学工程]