Seeing invisible objects with intelligent optics

Isaac Nape; Andrew Forbes

doi:10.1038/s41377-024-01575-2

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Seeing invisible objects with intelligent optics

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- Seeing invisible objects with intelligent optics
- Seeing invisible objects with intelligent optics
- LSA 2024年13卷第10期页码：1995-1996
- Affiliations：
  
  School of Physics, University of the Witwatersrand, Johannesburg, South Africa
- Author bio：
  
  Isaac Nape (isaac.nape@wits.ac.za)
- Funds：
- DOI：10.1038/s41377-024-01575-2
  中图分类号：
- 网络出版日期：2024-09-05，
  
  纸质出版日期：2024-10-31
- Accepted：
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Seeing invisible objects with intelligent optics[J]. LSA, 2024,13(10):1995-1996.

Nape, I. & Forbes, A. Seeing invisible objects with intelligent optics. Light: Science & Applications, 13, 1995-1996 (2024).
Seeing invisible objects with intelligent optics[J]. LSA, 2024,13(10):1995-1996. DOI： 10.1038/s41377-024-01575-2.

Nape, I. & Forbes, A. Seeing invisible objects with intelligent optics. Light: Science & Applications, 13, 1995-1996 (2024). DOI： 10.1038/s41377-024-01575-2.

摘要

Abstract

Transparent objects are invisible to traditional cameras because they can only detect intensity fluctuations

necessitating the need for interferometry followed by computationally intensive digital image processing. Now it is shown that the necessary transformations can be performed optically by combining machine learning and diffractive optics

for a direct in-situ measurement of transparent objects with conventional cameras.

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Keywords

references

Li, J. X. et al. All-optical complex field imaging using diffractive processors. Light Sci. Appl. 13 , 120. https://doi.org/10.1038/s41377-024-01482-6 (2024)..

Sephton, B. et al. Revealing the embedded phase in single-pixel quantum ghost imaging. Optica 10 , 286–291 (2023)..

Popescu, G. Quantitative Phase Imaging of Cells and Tissues . (McGraw-Hill, New York, 2011).

Shechtman, Y. et al. Phase retrieval with application to optical imaging: a contemporary overview. IEEE Signal Process. Mag. 32 , 87–109 (2015)..

Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361 , 1004–1008 (2018)..

Ashtiani, F., Geers, A. J. & Aflatouni, F. An on-chip photonic deep neural network for image classification. Nature 606 , 501–506 (2022)..

Mengu, D. & Ozcan, A. All‐optical phase recovery: diffractive computing for quantitative phase imaging. Adv. Optical Mater. 10 , 2200281 (2022)..

Wiecha, P. R. et al. Deep learning in nano-photonics: inverse design and beyond. Photonics Res. 9 , B182–B200 (2021)..

Ren, H. R. et al. Three-dimensional vectorial holography based on machine learning inverse design. Sci. Adv. 6 , eaaz4261 (2020)..

Krenn, M. et al. Conceptual understanding through efficient automated design of quantum optical experiments. Phys. Rev. X 11 , 031044 (2021)..

Forbes, A., De Oliveira, M. & Dennis, M. R. Structured light. Nat. Photonics 15 , 253–262 (2021)..

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