Single-pixel machine vision using spectral encoding through diffractive optical networks

Jingxi Li; Deniz Mengu; Nezih T. Yardimci; Yi Luo; Xurong Li; Muhammed Veli; Yair Rivenson; Mona Jarrahi; Aydogan Ozcan

Single-pixel machine vision using spectral encoding through diffractive optical networks

Jingxi Li
, Deniz Mengu
, Nezih T. Yardimci
, Yi Luo
, Xurong Li
, Muhammed Veli
, Yair Rivenson
, Mona Jarrahi
, Aydogan Ozcan

Research output: Contribution to journal › Conference article › peer-review

Abstract

We present and experimentally demonstrate a deep learning-driven machine-vision framework that trains diffractive surfaces to encode the spatial information objects into the output power spectrum for all-optical image classification using a single-pixel spectroscopic detector.

Original language	English
Article number	FM3L.8
Journal	Optics InfoBase Conference Papers
State	Published - 1 Jan 2021
Externally published	Yes
Event	CLEO: QELS_Fundamental Science, CLEO: QELS 2021 - Part of Conference on Lasers and Electro-Optics, CLEO 2021 - Virtual, Online, United States Duration: 9 May 2021 → 14 May 2021

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

Cite this

@article{31939f7863a24a169915f5555bf62367,

title = "Single-pixel machine vision using spectral encoding through diffractive optical networks",

abstract = "We present and experimentally demonstrate a deep learning-driven machine-vision framework that trains diffractive surfaces to encode the spatial information objects into the output power spectrum for all-optical image classification using a single-pixel spectroscopic detector.",

author = "Jingxi Li and Deniz Mengu and Yardimci, \{Nezih T.\} and Yi Luo and Xurong Li and Muhammed Veli and Yair Rivenson and Mona Jarrahi and Aydogan Ozcan",

note = "Publisher Copyright: {\textcopyright} OSA 2021, {\textcopyright} 2021 The Author(s); CLEO: QELS\_Fundamental Science, CLEO: QELS 2021 - Part of Conference on Lasers and Electro-Optics, CLEO 2021 ; Conference date: 09-05-2021 Through 14-05-2021",

year = "2021",

month = jan,

day = "1",

language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

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TY - JOUR

T1 - Single-pixel machine vision using spectral encoding through diffractive optical networks

AU - Li, Jingxi

AU - Mengu, Deniz

AU - Yardimci, Nezih T.

AU - Luo, Yi

AU - Li, Xurong

AU - Veli, Muhammed

AU - Rivenson, Yair

AU - Jarrahi, Mona

AU - Ozcan, Aydogan

PY - 2021/1/1

Y1 - 2021/1/1

N2 - We present and experimentally demonstrate a deep learning-driven machine-vision framework that trains diffractive surfaces to encode the spatial information objects into the output power spectrum for all-optical image classification using a single-pixel spectroscopic detector.

AB - We present and experimentally demonstrate a deep learning-driven machine-vision framework that trains diffractive surfaces to encode the spatial information objects into the output power spectrum for all-optical image classification using a single-pixel spectroscopic detector.

UR - https://www.scopus.com/pages/publications/85119444471

M3 - Conference article

AN - SCOPUS:85119444471

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - FM3L.8

T2 - CLEO: QELS_Fundamental Science, CLEO: QELS 2021 - Part of Conference on Lasers and Electro-Optics, CLEO 2021

Y2 - 9 May 2021 through 14 May 2021

ER -

Single-pixel machine vision using spectral encoding through diffractive optical networks

Abstract

ASJC Scopus subject areas

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