Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
Department of Electrical Engineering, University of Hawai'i at Manoa, Honolulu, HI, USA.
Nat Commun. 2021 Apr 23;12(1):2413. doi: 10.1038/s41467-021-22696-x.
Recent years have seen the rapid growth of new approaches to optical imaging, with an emphasis on extracting three-dimensional (3D) information from what is normally a two-dimensional (2D) image capture. Perhaps most importantly, the rise of computational imaging enables both new physical layouts of optical components and new algorithms to be implemented. This paper concerns the convergence of two advances: the development of a transparent focal stack imaging system using graphene photodetector arrays, and the rapid expansion of the capabilities of machine learning including the development of powerful neural networks. This paper demonstrates 3D tracking of point-like objects with multilayer feedforward neural networks and the extension to tracking positions of multi-point objects. Computer simulations further demonstrate how this optical system can track extended objects in 3D, highlighting the promise of combining nanophotonic devices, new optical system designs, and machine learning for new frontiers in 3D imaging.
近年来，光学成像的新方法迅速发展，重点是从通常的二维（2D）图像捕获中提取三维（3D）信息。也许最重要的是，计算成像的兴起使光学组件的新物理布局和新算法都能够得以实现。本文涉及两项进展的融合：使用石墨烯光电探测器阵列开发透明焦点堆叠成像系统，以及机器学习能力的快速扩展，包括强大的神经网络的开发。本文展示了使用多层前馈神经网络对点状物体进行 3D 跟踪，并扩展到对多点物体位置的跟踪。计算机模拟进一步演示了该光学系统如何在 3D 中跟踪扩展物体，突出了将纳米光子器件、新光学系统设计和机器学习相结合用于 3D 成像新前沿的潜力。
