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Compressed-Sensing-based Gradient Reconstruction for Ghost Imaging

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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.

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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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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Rong Zhu & Ying Guo

  2. School of Information Science and Engineering, Qufu Normal University, Rizhao, Shandong, 276826, China

    Guangshun Li

  3. Jiangsu Key Construction Laboratory of IoT Application Technology, Wuxi Taihu University, Wuxi, 214064, China

    Ying Guo

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  1. Rong Zhu
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  2. Guangshun Li
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  3. Ying Guo
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Correspondence to Rong Zhu or Ying Guo.

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Zhu, R., Li, G. & Guo, Y. Compressed-Sensing-based Gradient Reconstruction for Ghost Imaging. Int J Theor Phys 58, 1215–1226 (2019). https://doi.org/10.1007/s10773-019-04013-x

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  • DOI: https://doi.org/10.1007/s10773-019-04013-x

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