Optimized Design of Thick Holography Based on Deep Learning and Fourier Modal Method

Nanxing Chen; Yubin Cao; Jiandong Meng; Jianyi Li; Qingbo Yang; Kairui Cao

Optimized Design of Thick Holography Based on Deep Learning and Fourier Modal Method

Nanxing Chen^*
, Yubin Cao
, Jiandong Meng
, Jianyi Li
, Qingbo Yang
, Kairui Cao

^*Corresponding author for this work

School of Astronautics

Harbin Institute of Technology
CSCN
Zhongxing Telecommunication Equipment Corporation

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

Abstract

This paper introduces a deep neural network architecture, which is inspired by the Fourier modal method, facilitating layered iterative processing of thick holography to achieve high diffraction efficiency.

Original language	English
State	Published - 2024
Event	3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress - Toulouse, France Duration: 15 Jul 2024 → 19 Jul 2024

Conference

Conference	3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress
Country/Territory	France
City	Toulouse
Period	15/07/24 → 19/07/24

Cite this

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title = "Optimized Design of Thick Holography Based on Deep Learning and Fourier Modal Method",

abstract = "This paper introduces a deep neural network architecture, which is inspired by the Fourier modal method, facilitating layered iterative processing of thick holography to achieve high diffraction efficiency.",

author = "Nanxing Chen and Yubin Cao and Jiandong Meng and Jianyi Li and Qingbo Yang and Kairui Cao",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s).; 3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress ; Conference date: 15-07-2024 Through 19-07-2024",

year = "2024",

language = "英语",

}

TY - CONF

T1 - Optimized Design of Thick Holography Based on Deep Learning and Fourier Modal Method

AU - Chen, Nanxing

AU - Cao, Yubin

AU - Meng, Jiandong

AU - Li, Jianyi

AU - Yang, Qingbo

AU - Cao, Kairui

PY - 2024

Y1 - 2024

N2 - This paper introduces a deep neural network architecture, which is inspired by the Fourier modal method, facilitating layered iterative processing of thick holography to achieve high diffraction efficiency.

AB - This paper introduces a deep neural network architecture, which is inspired by the Fourier modal method, facilitating layered iterative processing of thick holography to achieve high diffraction efficiency.

UR - https://www.scopus.com/pages/publications/85204924907

M3 - 论文

AN - SCOPUS:85204924907

T2 - 3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress

Y2 - 15 July 2024 through 19 July 2024

ER -

Optimized Design of Thick Holography Based on Deep Learning and Fourier Modal Method

Abstract

Conference

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Cite this