High-quality ghost imaging based on undersampled natural-order Hadamard source

刘炕 ¹周成 ²黄继鹏 ²秦宏伍 ¹刘轩 ³李鑫伟 ⁴宋立军⁵

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作者信息

1. School of Electronic Information Engineering,Changchun University,Changchun 130022,China
2. School of Physics,Northeast Normal University,Changchun 130024,China
3. College of Electronic and Information Engineering,Changchun University of Science and Technology,Changchun 130022,China
4. Jilin Engineering Laboratory for Quantum Information Technology,Jilin Engineering Normal University,Changchun 130052,China
5. Changchun Institute of Technology,Changchun 130012,China
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Abstract

Improving the speed of ghost imaging is one of the main ways to leverage its advantages in sensitivity and imperfect spectral regions for practical applications.Because of the proportional relationship between image resolution and mea-surement time,when the image pixels are large,the measurement time increases,making it difficult to achieve real-time imaging.Therefore,a high-quality ghost imaging method based on undersampled natural-order Hadamard is proposed.This method uses the characteristics of the Hadamard matrix under undersampling conditions where image information can be fully obtained but overlaps,as well as deep learning to extract aliasing information from the overlapping results to obtain the true original image information.We conducted numerical simulations and experimental tests on binary and grayscale objects under undersampling conditions to demonstrate the effectiveness and scalability of this method.This method can significantly reduce the number of measurements required to obtain high-quality image information and advance application promotion.

Key words

ghost imaging/natural-order Hadamard/deep learning

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基金项目

Science and Technology Development Plan Project of Jilin Province,China(20220204134YY)

National Natural Science Foundation of China(62301140)

Project of the Education Department of Jilin Province(JJKH20231292KJ)

Project of the Education Department of Jilin Province(JJKH20240242KJ)

Program for Science and Technology Development of Changchun City(23YQ11)

Innovation and Entrepreneurship Talent Funding Project of Jilin Province(2023RY17)

Project of Jilin Provincial Development and Reform Commission(2023C042-4)

出版年

2024

中国物理B(英文版)

中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDEI

影响因子：0.995

ISSN：1674-1056

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