In dual-wavelength interferometry, the key issue is how to efficiently retrieve the phases at each wavelength using the minimum number of wavelength-multiplexed interferograms. To address this problem, a new dual-wavelength interferogram decoupling method with the help of deep learning is proposed in this study. This method requires only three randomly phase-shifted dual-wavelength interferograms. With a well-trained deep neural network, one can obtain three interferograms with arbitrary phase shifts at each wavelength. Using these interferograms, the wrapped phases of a single wavelength can be extracted, respectively, via an iterative phase retrieval algorithm, and then the phases at different synthetic beat wavelengths can be calculated. The feasibility and applicability of the proposed method are demonstrated by simulation experiments of the spherical cap and red blood cell, respectively. This method will provide a solution for the problem of phase retrieval in multiwavelength interferometry.

中文翻译：

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基于深度学习的三帧广义双波长移相干涉测量的双波长干涉图解耦方法

在双波长干涉测量中，关键问题是如何使用最少数量的波长复用干涉图有效地检索每个波长的相位。针对这一问题，本研究提出了一种借助深度学习的双波长干涉图解耦新方法。这种方法只需要三个随机相移的双波长干涉图。使用训练有素的深度神经网络，可以得到每个波长具有任意相移的三个干涉图。使用这些干涉图，可以通过迭代相位检索算法分别提取单个波长的缠绕相位，然后可以计算不同合成拍频波长的相位。分别通过球冠和红细胞的模拟实验证明了该方法的可行性和适用性。该方法将为多波长干涉测量中的相位恢复问题提供解决方案。