基于卷积神经网络的复光场模式分解
Modal decomposition of complex optical fields using convolutional neural networks
摘要：
最近的研究表明，卷积神经网络（CNN）可以被训练成使用光场的强度图像来执行模态分解。这些技术的一个基本限制是模态相位不能使用单个强度图像唯一地计算。模式相位的知识对于波前传感、对准和模式匹配应用是至关重要的。外差成像技术可以获得高分辨率和高帧频的激光束横向复振幅和相位分布图像。在这项工作中，我们训练一个CNN使用模拟外差图像进行模态分解，允许预测完整的模态相位。据我们所知，这是第一个利用复相位信息来执行模态分解的机器学习分解方案。我们将我们的网络与传统的重叠积分和质心定心算法进行了比较，结果表明，它对光束定心不太敏感，平均而言更准确。
英文摘要：
Recent studies have shown convolutional neural networks (CNNs) can be trained to perform modal decomposition using intensity images of optical fields. A fundamental limitation of these techniques is that the modal phases can not be uniquely calculated using a single intensity image. The knowledge of modal phases is crucial for wavefront sensing, alignment and mode matching applications. Heterodyne imaging techniques can provide images of the transverse complex amplitude & phase profile of laser beams at high resolutions and frame rates. In this work we train a CNN to perform modal decomposition using simulated heterodyne images, allowing the complete modal phases to be predicted. This is to our knowledge the first machine learning decomposition scheme to utilize complex phase information to perform modal decomposition. We compare our network with a traditional overlap integral & center-of-mass centering algorithm and show that it is both less sensitive to beam centering and on average more accurate.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
