Phase retrieval from fringe images is essential to many optical metrology applications. In the field of fringe projection profilometry, the phase is often obtained with systematic errors if the fringe pattern is not a perfect sinusoid. Several factors can account for non-sinusoidal fringe patterns, such as the non-linear input–output response (e.g., the gamma effect) of digital projectors, the residual harmonics in binary defocusing projection, and the image saturation due to intense reflection. Traditionally, these problems are handled separately with different well-designed methods, which can be seen as “one-to-one” strategies. Inspired by recent successful artificial intelligence-based optical imaging applications, we propose a “one-to-many” deep learning technique that can analyze non-sinusoidal fringe images resulting from different non-sinusoidal factors and even the coupling of these factors. We show for the first time, to the best of our knowledge, a trained deep neural network can effectively suppress the phase errors due to various kinds of non-sinusoidal patterns. Our work paves the way to robust and powerful learning-based fringe analysis approaches.

中文翻译：

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使用深度学习进行非正弦条纹分析的通用框架

从条纹图像中检索相位对于许多光学计量应用至关重要。在条纹投影轮廓测量领域，如果条纹图案不是完美的正弦曲线，则相位通常会带有系统误差。有几个因素可以解释非正弦条纹图案，例如数字投影仪的非线性输入-输出响应（例如伽马效应）、二进制散焦投影中的残余谐波以及由于强反射导致的图像饱和。传统上，这些问题是用不同的精心设计的方法分开处理的，这可以看作是“一对一”的策略。受最近成功的基于人工智能的光学成像应用的启发，我们提出了一种“一对多”深度学习技术，可以分析由不同非正弦因素甚至这些因素的耦合产生的非正弦条纹图像。据我们所知，我们首次展示了训练有素的深度神经网络可以有效抑制由于各种非正弦模式引起的相位误差。我们的工作为稳健而强大的基于学习的边缘分析方法铺平了道路。