All-optical geometric image transformations enabled by ultrathin metasurfaces

Xingwang Zhang, Xiaojie Zhang, Yao Duan, Lidan Zhang and Xingjie Ni ()
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Xingwang Zhang: The Pennsylvania State University
Xiaojie Zhang: The Pennsylvania State University
Yao Duan: The Pennsylvania State University
Lidan Zhang: The Pennsylvania State University
Xingjie Ni: The Pennsylvania State University

Nature Communications, 2023, vol. 14, issue 1, 1-8

Abstract: Abstract Image processing plays a vital role in artificial visual systems, which have diverse applications in areas such as biomedical imaging and machine vision. In particular, optical analog image processing is of great interest because of its parallel processing capability and low power consumption. Here, we present ultra-compact metasurfaces performing all-optical geometric image transformations, which are essential for image processing to correct image distortions, create special image effects, and morph one image into another. We show that our metasurfaces can realize binary image transformations by modifying the spatial relationship between pixels and converting binary images from Cartesian to log-polar coordinates with unparalleled advantages for scale- and rotation-invariant image preprocessing. Furthermore, we extend our approach to grayscale image transformations and convert an image with Gaussian intensity profile into another image with flat-top intensity profile. Our technique will potentially unlock new opportunities for various applications such as target tracking and laser manufacturing.

Date: 2023
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DOI: 10.1038/s41467-023-43981-x

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