Neural network based 3D tracking with a graphene transparent focal stack imaging system

Dehui Zhang, Zhen Xu, Zhengyu Huang, Audrey Rose Gutierrez, Cameron J. Blocker, Che-Hung Liu, Miao-Bin Lien, Gong Cheng, Zhe Liu, Il Yong Chun (), Jeffrey A. Fessler (), Zhaohui Zhong () and Theodore B. Norris ()
Additional contact information
Dehui Zhang: University of Michigan
Zhen Xu: University of Michigan
Zhengyu Huang: University of Michigan
Audrey Rose Gutierrez: University of Michigan
Cameron J. Blocker: University of Michigan
Che-Hung Liu: University of Michigan
Miao-Bin Lien: University of Michigan
Gong Cheng: University of Michigan
Zhe Liu: University of Michigan
Il Yong Chun: University of Hawai’i at Manoa
Jeffrey A. Fessler: University of Michigan
Zhaohui Zhong: University of Michigan
Theodore B. Norris: University of Michigan

Nature Communications, 2021, vol. 12, issue 1, 1-7

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-22696-x

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