中国物理B ›› 2024, Vol. 33 ›› Issue (9): 94204-094204.doi: 10.1088/1674-1056/ad62e1

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

Kang Liu(刘炕)1, Cheng Zhou(周成)2,†, Jipeng Huang(黄继鹏)2, Hongwu Qin(秦宏伍)1,‡, Xuan Liu(刘轩)3, Xinwei Li(李鑫伟)4, and Lijun Song(宋立军)5,§   

  1. 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
  • 收稿日期:2024-05-22 修回日期:2024-07-12 接受日期:2024-07-15 发布日期:2024-09-11
  • 通讯作者: Cheng Zhou, Hongwu Qin, Hongwu Qin E-mail:zhoucheng91210@163.com;qinhongwu@ccu.edu.cn;ccdxslj@ccu.edu.cn
  • 基金资助:
    Project supported by the Science and Technology Development Plan Project of Jilin Province, China (Grant No. 20220204134YY), the National Natural Science Foundation of China (Grant No. 62301140), Project of the Education Department of Jilin Province (Grant Nos. JJKH20231292KJ and JJKH20240242KJ), Program for Science and Technology Development of Changchun City (Grant No. 23YQ11), Innovation and Entrepreneurship Talent Funding Project of Jilin Province (Grant No. 2023RY17), and the Project of Jilin Provincial Development and Reform Commission (Grant No. 2023C042-4).

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

Kang Liu(刘炕)1, Cheng Zhou(周成)2,†, Jipeng Huang(黄继鹏)2, Hongwu Qin(秦宏伍)1,‡, Xuan Liu(刘轩)3, Xinwei Li(李鑫伟)4, and Lijun Song(宋立军)5,§   

  1. 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
  • Received:2024-05-22 Revised:2024-07-12 Accepted:2024-07-15 Published:2024-09-11
  • Contact: Cheng Zhou, Hongwu Qin, Hongwu Qin E-mail:zhoucheng91210@163.com;qinhongwu@ccu.edu.cn;ccdxslj@ccu.edu.cn
  • Supported by:
    Project supported by the Science and Technology Development Plan Project of Jilin Province, China (Grant No. 20220204134YY), the National Natural Science Foundation of China (Grant No. 62301140), Project of the Education Department of Jilin Province (Grant Nos. JJKH20231292KJ and JJKH20240242KJ), Program for Science and Technology Development of Changchun City (Grant No. 23YQ11), Innovation and Entrepreneurship Talent Funding Project of Jilin Province (Grant No. 2023RY17), and the Project of Jilin Provincial Development and Reform Commission (Grant No. 2023C042-4).

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摘要： 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 measurement 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.

关键词: ghost imaging, natural-order Hadamard, deep learning

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 measurement 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

中图分类号:  (Imaging and optical processing)

  • 42.30.-d
42.30.Va (Image forming and processing) 42.30.Wb (Image reconstruction; tomography)