Edge detection with meta-lens: From one dimension to three dimensions

Mu Ku Chen; Yue Yan; Xiaoyuan Liu; Yongfeng Wu; Jingcheng Zhang; Jiaqi Yuan; Zhengnan Zhang; Din Ping Tsai

doi:10.1515/nanoph-2021-0239

Edge detection with meta-lens : From one dimension to three dimensions

Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review

Overview

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Author(s)

Yue Yan
Yongfeng Wu
Jingcheng Zhang
Jiaqi Yuan
Zhengnan Zhang

Detail(s)

Original language	English
Journal / Publication	Nanophotonics
Online published	22 Jul 2021
Publication status	Online published - 22 Jul 2021
Externally published	Yes

Link(s)

DOI	DOI https://doi.org/10.1515/nanoph-2021-0239 Final Published version
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Attachment(s)	Documents 82616928.pdf Final Published version, 1.93 MB, PDF Publisher's Copyright Statement This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
Link to Scopus	https://www.scopus.com/record/display.uri?eid=2-s2.0-85111645912&origin=recordpage
Permanent Link	https://scholars.cityu.edu.hk/en/publications/publication(c8e46c60-a596-4297-bfbf-b048cf2c17ca).html

Abstract

Meta-lens has successfully been developed for a variety of optical functions. We demonstrate a light-field edge detection imaging system with a gallium nitride achromatic meta-lens array. It enables edge detection from one dimension to three dimensions. The designed meta-lens array consists of 60 by 60 achromatic meta-lenses, which operate in the visible range from 400 to 660 nm. All of the light field information of objects in the scene can be captured and computed. The focused edge images from one dimension to three dimensions are extracted with depth estimation by image rendering. Three dimensions edge detection is two dimensions edge imaging with depth information. The focused edge images can be obtained by the sub-image reconstruction of the light field image. Our multidimensional edge detection system by achromatic meta-lens array brings novel advantages, such as broadband detection, data volume reduction, and device miniaturization capacity. Results of our experiments show new insight into applications of biological diagnose and robotic vision.