AI-assisted superresolution of two practical point sources

Abdelali Sajia; Bilal Benzimoun; Pawan Khatiwada; Guogan Zhao; Reshawna Curley; Xiao Feng Qian

AI-assisted superresolution of two practical point sources

Abdelali Sajia, Bilal Benzimoun, Pawan Khatiwada, Guogan Zhao, Reshawna Curley, Xiao Feng Qian

Department of Physics

Research output: Contribution to conference › Paper › peer-review

Abstract

We investigate super-resolution of two spatially separated practical point sources using machine learning. High fidelity of over 90% is achieved for separations that are 16 times smaller than the conventional resolution limit.

Original language	English
State	Published - 2024
Event	2024 Frontiers in Optics, FiO 2024 - Denver, United States Duration: 23 Sep 2024 → 26 Sep 2024

Conference

Conference	2024 Frontiers in Optics, FiO 2024
Country/Territory	United States
City	Denver
Period	23/09/24 → 26/09/24

Cite this

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title = "AI-assisted superresolution of two practical point sources",

abstract = "We investigate super-resolution of two spatially separated practical point sources using machine learning. High fidelity of over 90% is achieved for separations that are 16 times smaller than the conventional resolution limit.",

author = "Abdelali Sajia and Bilal Benzimoun and Pawan Khatiwada and Guogan Zhao and Reshawna Curley and Qian, {Xiao Feng}",

note = "Publisher Copyright: {\textcopyright} Optica Publishing Group 2024, {\textcopyright} 2024 The Author(s); 2024 Frontiers in Optics, FiO 2024 ; Conference date: 23-09-2024 Through 26-09-2024",

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language = "English",

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TY - CONF

T1 - AI-assisted superresolution of two practical point sources

AU - Sajia, Abdelali

AU - Benzimoun, Bilal

AU - Khatiwada, Pawan

AU - Zhao, Guogan

AU - Curley, Reshawna

AU - Qian, Xiao Feng

PY - 2024

Y1 - 2024

N2 - We investigate super-resolution of two spatially separated practical point sources using machine learning. High fidelity of over 90% is achieved for separations that are 16 times smaller than the conventional resolution limit.

AB - We investigate super-resolution of two spatially separated practical point sources using machine learning. High fidelity of over 90% is achieved for separations that are 16 times smaller than the conventional resolution limit.

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UR - http://www.scopus.com/inward/citedby.url?scp=85215980815&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85215980815

T2 - 2024 Frontiers in Optics, FiO 2024

Y2 - 23 September 2024 through 26 September 2024

ER -

AI-assisted superresolution of two practical point sources

Abstract

Conference

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