Coded-Aperture imaging based on random code and Backpropagation neural network

Zeyu Wang; Chao Wang; Pin Gong; Liansheng Li; Zhimeng Hu; Yongqiang Shi; Xiaolei Shen; Cheng Zhou; Xiaoxiang Zhu; Xiaobin Tang

doi:10.1016/j.anucene.2023.110183

Coded-Aperture imaging based on random code and Backpropagation neural network

Zeyu Wang, Chao Wang, Pin Gong, Liansheng Li, Zhimeng Hu, Yongqiang Shi, Xiaolei Shen, Cheng Zhou, Xiaoxiang Zhu, Xiaobin Tang

Research output: Contribution to journal › Article › peer-review

Abstract

Coded-aperture radiation imaging technology has important application value in the field of space radiation detection. A coded-aperture imaging algorithm based on random code and Backpropagation neural network (BPNN) is proposed for high quality imaging of x-rays and gamma rays in space environments. The experimental results show that BPNN can improve the signal-to-noise ratio of the reconstructed image when applied to the reconstruction process of coded-aperture imaging based on random code. Therefore, it has a good application prospect in the field of space coded-aperture imaging.

Original language	English
Article number	110183
Journal	Annals of Nuclear Energy
Volume	195
DOIs	https://doi.org/10.1016/j.anucene.2023.110183
State	Published - Jan 2024
Externally published	Yes

Keywords

BPNN
Coded-aperture imaging
Monte Carlo simulation
Random code

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10.1016/j.anucene.2023.110183

Cite this

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title = "Coded-Aperture imaging based on random code and Backpropagation neural network",

abstract = "Coded-aperture radiation imaging technology has important application value in the field of space radiation detection. A coded-aperture imaging algorithm based on random code and Backpropagation neural network (BPNN) is proposed for high quality imaging of x-rays and gamma rays in space environments. The experimental results show that BPNN can improve the signal-to-noise ratio of the reconstructed image when applied to the reconstruction process of coded-aperture imaging based on random code. Therefore, it has a good application prospect in the field of space coded-aperture imaging.",

keywords = "BPNN, Coded-aperture imaging, Monte Carlo simulation, Random code",

author = "Zeyu Wang and Chao Wang and Pin Gong and Liansheng Li and Zhimeng Hu and Yongqiang Shi and Xiaolei Shen and Cheng Zhou and Xiaoxiang Zhu and Xiaobin Tang",

note = "Publisher Copyright: {\textcopyright} 2023 Elsevier Ltd",

year = "2024",

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doi = "10.1016/j.anucene.2023.110183",

language = "English",

volume = "195",

journal = "Annals of Nuclear Energy",

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T1 - Coded-Aperture imaging based on random code and Backpropagation neural network

AU - Wang, Zeyu

AU - Wang, Chao

AU - Gong, Pin

AU - Li, Liansheng

AU - Hu, Zhimeng

AU - Shi, Yongqiang

AU - Shen, Xiaolei

AU - Zhou, Cheng

AU - Zhu, Xiaoxiang

AU - Tang, Xiaobin

PY - 2024/1

Y1 - 2024/1

N2 - Coded-aperture radiation imaging technology has important application value in the field of space radiation detection. A coded-aperture imaging algorithm based on random code and Backpropagation neural network (BPNN) is proposed for high quality imaging of x-rays and gamma rays in space environments. The experimental results show that BPNN can improve the signal-to-noise ratio of the reconstructed image when applied to the reconstruction process of coded-aperture imaging based on random code. Therefore, it has a good application prospect in the field of space coded-aperture imaging.

AB - Coded-aperture radiation imaging technology has important application value in the field of space radiation detection. A coded-aperture imaging algorithm based on random code and Backpropagation neural network (BPNN) is proposed for high quality imaging of x-rays and gamma rays in space environments. The experimental results show that BPNN can improve the signal-to-noise ratio of the reconstructed image when applied to the reconstruction process of coded-aperture imaging based on random code. Therefore, it has a good application prospect in the field of space coded-aperture imaging.

KW - BPNN

KW - Coded-aperture imaging

KW - Monte Carlo simulation

KW - Random code

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DO - 10.1016/j.anucene.2023.110183

M3 - Article

AN - SCOPUS:85174335431

SN - 0306-4549

VL - 195

JO - Annals of Nuclear Energy

JF - Annals of Nuclear Energy

M1 - 110183

ER -

Coded-Aperture imaging based on random code and Backpropagation neural network

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Keywords

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