In this paper, we analyze a machine-learning-based non-iterative phase retrieval method. Phase retrieval and its applications have been attractive research topics in optics and photonics, for example, in biomedical imaging, astronomical imaging, and so on. Most conventional phase retrieval methods have used iterative processes to recover phase information; however, the calculation speed and convergence with these methods are serious issues in real-time monitoring applications. Machine-learning-based methods are promising for addressing these issues. Here, we numerically compare conventional methods and a machine-learning-based method in which a convolutional neural network is employed. Simulations with several conditions show that the machine-learning-based method realizes fast and robust phase recovery compared with the conventional methods. We also numerically demonstrate machine-learning-based phase retrieval from noisy measurements with a noisy training data set for improving the noise robustness. The machine-learning-based approach used in this study may increase the impact of phase retrieval, which is useful in various fields, where phase retrieval has been used as a fundamental tool.

中文翻译：

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基于机器学习的非迭代相位检索分析

在本文中，我们分析了一种基于机器学习的非迭代相位检索方法。相位检索及其应用一直是光学和光子学领域的有吸引力的研究主题，例如生物医学成像，天文成像等。大多数常规的相位检索方法都使用迭代过程来恢复相位信息。但是，这些方法的计算速度和收敛性是实时监控应用中的严重问题。基于机器学习的方法有望解决这些问题。在这里，我们在数值上比较了传统方法和基于卷积神经网络的基于机器学习的方法。在多种条件下的仿真表明，与传统方法相比，基于机器学习的方法实现了快速而稳定的相位恢复。我们还通过数值演示了从噪声测量中使用基于噪声的训练数据集进行基于机器学习的相位检索，以改善噪声的鲁棒性。本研究中使用的基于机器学习的方法可能会增加相位检索的影响，这在相位检索已被用作基本工具的各个领域都非常有用。