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International Journal of Theoretical Physics

, Volume 58, Issue 4, pp 1215–1226 | Cite as

Compressed-Sensing-based Gradient Reconstruction for Ghost Imaging

  • Rong ZhuEmail author
  • Guangshun Li
  • Ying GuoEmail author
Article
  • 158 Downloads

Abstract

In this paper, we propose a compression sensing ghost imaging algorithm to reduce the computation time with high image quality via compression sensing based on the total variation reconstruction. A small amount of measurements can be used for shortening the sampling time. The total variation is used as criteria during the search process. It makes the ghost image achieving a high image reconstruction quality. The simulation results demonstrate that the proposed method can enhance imaging quality with the reduced computation time.

Keywords

Compressed sensing Ghost imaging Gradient Total variation 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61572529, 61871407, 61872390, 61801522), and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 18KJB510045).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.School of Information Science and EngineeringQufu Normal UniversityRizhaoChina
  3. 3.Jiangsu Key Construction Laboratory of IoT Application TechnologyWuxi Taihu UniversityWuxiChina

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