论文标题
通过全光衍射神经网络对稀疏光圈系统的活塞感测
Piston sensing for sparse aperture systems via all-optical diffractive neural network
论文作者
论文摘要
在稀疏孔径成像区域实现实时活塞校正是一个至关重要的问题。本文介绍了一种基于光的神经网络感应方法，该方法可以实现光速感测。通过使用可检测的强度代表活塞，该提出的方法能够将影像光场的复杂振幅分布直接转换为活塞值。与电神经网络不同，强度表示的方式使该方法能够在无需成像采集和电气处理过程的情况下获得预测的活塞。模拟证明了该方法的点源可行性，并且对于单色光和宽带光都可以实现高精度。这种方法可以大大改善活塞感测的实时性能，并有助于稀疏孔径系统的发展。
It is a crucial issue to realize real-time piston correction in the area of sparse aperture imaging. This paper introduces an optical diffractive neural network-based piston sensing method, which can achieve light-speed sensing. By using detectable intensity to represent pistons, the proposed method is capable of converting complex amplitude distribution of the imaging optical field into piston values directly. Differing from the electrical neural network, the way of intensity representation enables the method to obtain the predicted pistons without imaging acquisition and electrical processing process. The simulations demonstrate the feasibility of the method for point source, and high accuracies are achieved for both monochromatic light and broadband light. This method can greatly improve the real-time performance of piston sensing and contribute to the development of the sparse aperture system.
