In recent years, there has been tremendous progress in the development of deep-learning-based approaches for optical metrology, which introduce various deep neural networks (DNNs) for many optical metrology tasks, such as fringe analysis, phase unwrapping, and digital image correlation. However, since different DNN models have their own strengths and limitations, it is difficult for a single DNN to make reliable predictions under all possible scenarios. In this work, we introduce ensemble learning into optical metrology, which combines the predictions of multiple DNNs to significantly enhance the accuracy and reduce the generalization error for the task of fringe-pattern analysis. First, several state-of-the-art base models of different architectures are selected. A K-fold average ensemble strategy is developed to train each base model multiple times with different data and calculate the mean prediction within each base model. Next, an adaptive ensemble strategy is presented to further combine the base models by building an extra DNN to fuse the features extracted from these mean predictions in an adaptive and fully automatic way. Experimental results demonstrate that ensemble learning could attain superior performance over state-of-the-art solutions, including both classic and conventional single-DNN-based methods. Our work suggests that by resorting to collective wisdom, ensemble learning offers a simple and effective solution for overcoming generalization challenges and boosts the performance of data-driven optical metrology methods.

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。

条纹图处理是光学测量技术中一个非常重要的步骤。从早期的经典傅里叶变换，到随后引入局部分析能力的窗口傅里叶变换、小波变换、S变换，再到近些年变分模型分解、经验模态分解（EMD）等，条纹图处理技术经历了长足的研究和发展历程。在这些优秀的技术中，EMD算法由于具有较强的自适应性和复杂信号处理能力而在近些年受到一定的关注。文中结合条纹图处理的关键内容和发展历程，重点分析和总结了EMD算法及其应用于条纹图处理的关键问题和研究进展，指出了该技术尚存的技术难点和主要问题，为相关技术的发展提供了理论和实践的参考。