Flat optics for image differentiation
Abstract
Image processing has become a critical technology in a variety of science and engineering disciplines. Although most image processing is performed digitally, optical analog processing has the advantages of being low-power and high-speed, but it requires a large volume. Here, we demonstrate flat optics for direct image differentiation, allowing us to significantly shrink the required optical system size. We first demonstrate how the differentiator can be combined with traditional imaging systems such as a commercial optical microscope and camera sensor for edge detection with a numerical aperture up to 0.32. We next demonstrate how the entire processing system can be realized as a monolithic compound flat optic by integrating the differentiator with a metalens. The compound nanophotonic system manifests the advantage of thin form factor as well as the ability to implement complex transfer functions, and could open new opportunities in applications such as biological imaging and computer vision.
- Authors:
-
[2];
[1]
- Vanderbilt Univ., Nashville, TN (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1619041
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Photonics
- Additional Journal Information:
- Journal Volume: 14; Journal Issue: 5; Journal ID: ISSN 1749-4885
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Zhou, You, Zheng, Hanyu, Kravchenko, Ivan I., and Valentine, Jason. Flat optics for image differentiation. United States: N. p., 2020.
Web. doi:10.1038/s41566-020-0591-3.
Zhou, You, Zheng, Hanyu, Kravchenko, Ivan I., & Valentine, Jason. Flat optics for image differentiation. United States. https://doi.org/10.1038/s41566-020-0591-3
Zhou, You, Zheng, Hanyu, Kravchenko, Ivan I., and Valentine, Jason. Mon .
"Flat optics for image differentiation". United States. https://doi.org/10.1038/s41566-020-0591-3. https://www.osti.gov/servlets/purl/1619041.
@article{osti_1619041,
title = {Flat optics for image differentiation},
author = {Zhou, You and Zheng, Hanyu and Kravchenko, Ivan I. and Valentine, Jason},
abstractNote = {Image processing has become a critical technology in a variety of science and engineering disciplines. Although most image processing is performed digitally, optical analog processing has the advantages of being low-power and high-speed, but it requires a large volume. Here, we demonstrate flat optics for direct image differentiation, allowing us to significantly shrink the required optical system size. We first demonstrate how the differentiator can be combined with traditional imaging systems such as a commercial optical microscope and camera sensor for edge detection with a numerical aperture up to 0.32. We next demonstrate how the entire processing system can be realized as a monolithic compound flat optic by integrating the differentiator with a metalens. The compound nanophotonic system manifests the advantage of thin form factor as well as the ability to implement complex transfer functions, and could open new opportunities in applications such as biological imaging and computer vision.},
doi = {10.1038/s41566-020-0591-3},
journal = {Nature Photonics},
number = 5,
volume = 14,
place = {United States},
year = {Mon Feb 24 00:00:00 EST 2020},
month = {Mon Feb 24 00:00:00 EST 2020}
}
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