Skip to main content
Log in

High resolution reconstruction method of ghost imaging via SURF-NSML

  • Original Paper - Atoms, Molecules and Optics
  • Published:
Journal of the Korean Physical Society Aims and scope Submit manuscript

Abstract

To meet the visual characteristics of human eyes, high resolution imaging technology came into being. In this paper, a high resolution reconstruction method of ghost imaging via SURF-NSML is proposed. Using the image registration method of Speeded Up Robust Features (SURF) and the fusion algorithm of New Sum of Modified Laplacian (NSML), a series of low-resolution images obtained by the ghost imaging system were registered and fused to obtain high-resolution images. This high resolution image reconstruction method does not need the use of spectroscopic devices, filters and other devices, simplifying the experimental equipment; and it is received by a bucket detector, which greatly increases the utilization of experimental equipment. By the reconstruction analysis of images from natural scenes and from the medical field, it is proved that this method combining the device platform with the language algorithm can reconstruct the target image with better visual characteristics and richer details, which has a good promotion effect on the research of image reconstruction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. S. Rohani, D. Allen, B. Gare, High-resolution imaging of the human incudostapedial joint using synchrotron-radiation phase-contrast imaging. J. Microsc. 277, 61–70 (2020)

    Article  Google Scholar 

  2. L. Haochang, Y. Zhao, Y. Guang, High resolution imaging based on photo-emission electron microscopy excited by deep ultraviolet laser. Acta Physica Sinica Chin. Edition 69, 096801 (2020)

    Article  Google Scholar 

  3. D. Lowe, Distinctive image features from scale-invariant key points. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

  4. H. Hong, H. Qiqiang, L. Xiaojun, Hierarchical spatial pyramid max pooling based on SIFT features and sparse coding for image classification. IET Comput. Vis. 7, 144–150 (2013)

    Article  Google Scholar 

  5. S. Magdy, Y. Abouelseoud, M. Mikhail, Privacy preserving search index for image databases based on SURF and order preserving encryption. IET Image Process 14, 874–881 (2020)

    Article  Google Scholar 

  6. Y. Toda, H.H. Yz, T. Matsuno, Adaptive evolution strategy sample consensus for 3D reconstruction from two cameras. Artif. Life Robot. 25, 466–474 (2020)

    Article  Google Scholar 

  7. S. Xiaolong, W. Zhengyong, F. Yaoqing, Fast image fusion based on sum of modified Laplacian. Comput. Eng. Appl. 51, 193–197 (2015)

    Google Scholar 

  8. M. Paul-Antoine, P.A. Morris, T. Ermes, Experimental limits of ghost diffraction: Popper’s thought experiment. Sci. Rep. 8, 13183 (2018)

    Article  ADS  Google Scholar 

  9. P. Moreau, E. Toninelli, T. Gregory, Ghost imaging using optical correlations. Laser Photon. Rev. 12, 1700143 (2018)

    Article  ADS  Google Scholar 

  10. P. Ryczkowski, C.G. Amiot, J.M. Dudley, Experimental demonstration of spectral domain computational ghost imaging. Sci. Rep. 11, 8403 (2021)

    Article  Google Scholar 

  11. W. Le, Z. Shengmei, Fast reconstructed and high-quality ghost imaging with fast Walsh-Hadamard transform. Photon. Res. 4, 240–244 (2016)

    Article  Google Scholar 

  12. Y. Yu, C. Wang, J. Liu, Ghost imaging with different frequencies through non-degenerated four-wave mixing. Opt. Express 24, 18290 (2016)

    Article  ADS  Google Scholar 

  13. Y. Ya, W. Chengyuan, L. Jun, Ghost imaging with different frequencies through non-degenerated four-wave mixing. Opt. Express 24, 18290–18296 (2016)

    Article  ADS  Google Scholar 

  14. Y. Hualong, Z. Leihong, Z. Dawei, Non-imaging target recognition algorithm based on projection matrix and image Euclidean distance by computational ghost imaging. Opt. Laser Technol. 137, 106779 (2021)

    Article  Google Scholar 

  15. Z. Xiaonan, F. Xiquan, W. Bowen, Multipath effect constraint of pseudo-thermal light source in ghost imaging. Opt. Commun. 425, 185–189 (2018)

    Article  Google Scholar 

  16. W. Heng, W. Ruizhou, L. Changsheng, Influence of intensity fluctuations on Hadamard-based computational ghost imaging. Opt. Commun. 454, 124490 (2020)

    Article  Google Scholar 

  17. X. Wang, Y. Tao, F. Yang, An effective compressive computational ghost imaging with hybrid speckle pattern. Opt. Commun. 454, 124470 (2019)

    Article  Google Scholar 

  18. Y. O-Oka, S. Fukatsu, Differential ghost imaging in time domain. Appl. Phys. Lett. 6, 111 (2017)

    Google Scholar 

  19. A.M. Paniagua-Diaz, I. Starshynov, N. Fayard, Blind ghost imaging. Optica 6(4), 460 (2019)

    Article  ADS  Google Scholar 

  20. P.A. Moreau, E. Toninelli, P.A. Morris, Resolution limits of quantum ghost imaging. Opt. Express 26, 7528–7536 (2018)

    Article  ADS  Google Scholar 

  21. S. Liansheng, C. Yin, T. Ailing, An optical watermarking scheme with two-layer framework based on computational ghost imaging. Opt. Lasers Eng. 107, 38–45 (2018)

    Article  Google Scholar 

  22. S. Dongfeng, Z. Jiamin, H. Jian, Polarization-multiplexing ghost imaging. Opt. Lasers Eng. 102, 100–105 (2018)

    Article  Google Scholar 

  23. Y. Fei, C. Kehan, A.M. Iliyasu, Circuit-based modular implementation of quantum ghost imaging. IEEE Access 8, 23054–23068 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by National Natural Science Foundation of China (NSFC) 61775140.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haojie Sun.

Ethics declarations

Conflict of interest

We declare that we do not have any conflicts of interest that is related to the submitted work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, H., Kang, Y., Wang, J. et al. High resolution reconstruction method of ghost imaging via SURF-NSML. J. Korean Phys. Soc. 80, 964–971 (2022). https://doi.org/10.1007/s40042-022-00464-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40042-022-00464-4

Keywords

Navigation