• Acta Optica Sinica
  • Vol. 44, Issue 2, 0211002 (2024)
Xiongyu Du1、2, Qi Wang1, Guangzhou Ouyang1、*, Lingling Ma1, Zui Tao1, Fang Huang3, and Yifang Niu1
Author Affiliations
  • 1Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • 2School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
  • 3School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
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    DOI: 10.3788/AOS231507 Cite this Article Set citation alerts
    Xiongyu Du, Qi Wang, Guangzhou Ouyang, Lingling Ma, Zui Tao, Fang Huang, Yifang Niu. Quality Assessment Method of Ghost Imaging System Based on Communication Channel Model[J]. Acta Optica Sinica, 2024, 44(2): 0211002 Copy Citation Text show less
    Imaging process of ghost imaging
    Fig. 1. Imaging process of ghost imaging
    Comparison of communication system and ghost imaging system
    Fig. 2. Comparison of communication system and ghost imaging system
    Channel model for sampling process of ghost imaging
    Fig. 3. Channel model for sampling process of ghost imaging
    R independent sub-channels after SVD
    Fig. 4. R independent sub-channels after SVD
    Statistical characteristics analysis of imaging scene information. (a) Parts of imaging scene; (b) corresponding MSCN coefficient images; (c) fitting of MSCN coefficient distribution and Gaussian distribution of 100 imaging scene images (lines represent mean value, about 0.95); (d) parts of artificial image; (e) corresponding MSCN coefficient images; (f) MSCN coefficient distribution of 5 kinds of artificial images
    Fig. 5. Statistical characteristics analysis of imaging scene information. (a) Parts of imaging scene; (b) corresponding MSCN coefficient images; (c) fitting of MSCN coefficient distribution and Gaussian distribution of 100 imaging scene images (lines represent mean value, about 0.95); (d) parts of artificial image; (e) corresponding MSCN coefficient images; (f) MSCN coefficient distribution of 5 kinds of artificial images
    Variations of channel capacity and MSE with distribution type of matrix elements. (a) Imaging scene image used in simulation; (b) channel capacity; (c) MSE of reconstructed image under GPSR reconstruction algorithm; (d) MSE of reconstructed image under pseudo-inverse reconstruction algorithm
    Fig. 6. Variations of channel capacity and MSE with distribution type of matrix elements. (a) Imaging scene image used in simulation; (b) channel capacity; (c) MSE of reconstructed image under GPSR reconstruction algorithm; (d) MSE of reconstructed image under pseudo-inverse reconstruction algorithm
    Variations of channel capacity and MSE with number of samples. (a) Imaging scene images used in simulation; (b) channel capacity; (c) MSE of reconstructed image under GPSR reconstruction algorithm; (d) MSE of reconstructed image under pseudo-inverse reconstruction algorithm
    Fig. 7. Variations of channel capacity and MSE with number of samples. (a) Imaging scene images used in simulation; (b) channel capacity; (c) MSE of reconstructed image under GPSR reconstruction algorithm; (d) MSE of reconstructed image under pseudo-inverse reconstruction algorithm
    Comparison of normalized channel capacity and normalized inversion MSE under two reconstruction algorithms. (a) Three normalized curves; (b) GPSR reconstruction algorithm; (c) pseudo-inverse reconstruction algorithm
    Fig. 8. Comparison of normalized channel capacity and normalized inversion MSE under two reconstruction algorithms. (a) Three normalized curves; (b) GPSR reconstruction algorithm; (c) pseudo-inverse reconstruction algorithm
    Box-plots of all fits of 100 imaging scenes under two reconstruction algorithms. (a) GPSR reconstruction algorithm; (b) pseudo-inverse reconstruction algorithm
    Fig. 9. Box-plots of all fits of 100 imaging scenes under two reconstruction algorithms. (a) GPSR reconstruction algorithm; (b) pseudo-inverse reconstruction algorithm
    Xiongyu Du, Qi Wang, Guangzhou Ouyang, Lingling Ma, Zui Tao, Fang Huang, Yifang Niu. Quality Assessment Method of Ghost Imaging System Based on Communication Channel Model[J]. Acta Optica Sinica, 2024, 44(2): 0211002
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