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

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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.

ghost imagingnatural-order Hadamarddeep learning

刘炕、周成、黄继鹏、秦宏伍、刘轩、李鑫伟、宋立军

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School of Electronic Information Engineering,Changchun University,Changchun 130022,China

School of Physics,Northeast Normal University,Changchun 130024,China

College of Electronic and Information Engineering,Changchun University of Science and Technology,Changchun 130022,China

Jilin Engineering Laboratory for Quantum Information Technology,Jilin Engineering Normal University,Changchun 130052,China

Changchun Institute of Technology,Changchun 130012,China

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Science and Technology Development Plan Project of Jilin Province,ChinaNational Natural Science Foundation of ChinaProject of the Education Department of Jilin ProvinceProject of the Education Department of Jilin ProvinceProgram for Science and Technology Development of Changchun CityInnovation and Entrepreneurship Talent Funding Project of Jilin ProvinceProject of Jilin Provincial Development and Reform Commission

20220204134YY62301140JJKH20231292KJJJKH20240242KJ23YQ112023RY172023C042-4

2024

10.1088/1674-1056/ad62e1

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
年,卷(期)：2024.33(9)