Tan Ji, Su Wenqing, He Zhaoshui, Huang Naixing, Di Jianglei, Zhong Liyun, Bai Yulei, Dong Bo, Xie Shengli
Opt Express. 2022 Jul 4;30(14):24245-24260. doi: 10.1364/OE.461174.
The non-uniform motion-induced error reduction in dynamic fringe projection profilometry is complex and challenging. Recently, deep learning (DL) has been successfully applied to many complex optical problems with strong nonlinearity and exhibits excellent performance. Inspired by this, a deep learning-based method is developed for non-uniform motion-induced error reduction by taking advantage of the powerful ability of nonlinear fitting. First, a specially designed dataset of motion-induced error reduction is generated for network training by incorporating complex nonlinearity. Then, the corresponding DL-based architecture is proposed and it contains two parts: in the first part, a fringe compensation module is developed as network pre-processing to reduce the phase error caused by fringe discontinuity; in the second part, a deep neural network is employed to extract the high-level features of error distribution and establish a pixel-wise hidden nonlinear mapping between the phase with motion-induced error and the ideal one. Both simulations and real experiments demonstrate the feasibility of the proposed method in dynamic macroscopic measurement.
动态条纹投影轮廓术中非均匀运动引起的误差减小是复杂且具有挑战性的。近年来，深度学习（DL）已成功应用于许多具有强非线性的复杂光学问题，并展现出优异的性能。受此启发，利用非线性拟合的强大能力，开发了一种基于深度学习的方法来减小非均匀运动引起的误差。首先，通过纳入复杂非线性来生成用于网络训练的专门设计的运动误差减小数据集。然后，提出了相应的基于深度学习的架构，它包含两个部分：在第一部分中，开发了一个条纹补偿模块作为网络预处理，以减少由条纹不连续性引起的相位误差；在第二部分中，采用深度神经网络来提取误差分布的高级特征，并在存在运动引起误差的相位与理想相位之间建立逐像素的隐藏非线性映射。仿真和实际实验均证明了该方法在动态宏观测量中的可行性。
