J Opt Soc Am A Opt Image Sci Vis. 2021 Mar 1;38(3):321-327. doi: 10.1364/JOSAA.412433.
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.
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获取每个波长的相位。针对这个问题，本研究提出了一种新的基于深度学习的双波长干涉图去耦方法。该方法仅需要三个随机相移的双波长干涉图。通过训练有素的深度神经网络，可以获得每个波长的任意相移的三个干涉图。使用这些干涉图，可以通过迭代相位恢复算法分别提取单个波长的包裹相位，然后计算不同合成拍波波长的相位。通过对球帽和红细胞的模拟实验，验证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。
