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Title: 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:
 [1];  [1]; ORCiD logo [2]; ORCiD logo [1]
  1. Vanderbilt Univ., Nashville, TN (United States)
  2. 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. doi: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. doi: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 = {2020},
month = {2}
}

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