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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.
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This content will become publicly available on January 20, 2026
Advances in the photon avalanche luminescence of inorganic lanthanide-doped nanomaterials
Fundamental mechanisms, new applications (sub-diffraction imaging, optical thermometry or force sensing, and all-optical data storage and processing), new challenges and opportunities of photon avalanche highly non-linear nanomaterials are discussed.
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- Award ID(s):
- 2203510
- PAR ID:
- 10609758
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Chemical Society Reviews
- Volume:
- 54
- Issue:
- 2
- ISSN:
- 0306-0012
- Page Range / eLocation ID:
- 983 to 1026
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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