Abstract
Compressed ghost imaging method can effectively reduce the number of measurements required for ghost imaging reconstruction. The non-negative characteristics of measurement matrix in compressed ghost imaging method is inconsistent with the requirements of the measurement matrix in traditional compressed sensing theory, leading to low quality reconstruction. Aiming at the point, this paper proposes a singular value decomposition compressed ghost imaging method to improve the reconstruction quality of ghost imaging. First, the singular value decomposition is performed on the measurement matrix, and then the optimized measurement matrix and measurements are obtained, finally the reconstruction of the image is completed by the reconstruction algorithm. Numerical simulation experiments verify the superiority of our proposed singular value decomposition compressed ghost imaging method.
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28 March 2022
A Correction to this paper has been published: https://doi.org/10.1007/s00340-022-07804-z
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Acknowledgements
This project was supported by the Natural Science Foundation of Anhui Province (No. 2008085MF209), the Major Natural Science Foundation of Higher Education Institutions of Anhui Province (Nos. KJ2019ZD04, KJ2020ZD02), and Open Research Fund of Advanced Laser Technology Laboratory of Anhui Province (AHL2020KF05).
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Zhang, C., Tang, J., Zhou, J. et al. Singular value decomposition compressed ghost imaging. Appl. Phys. B 128, 47 (2022). https://doi.org/10.1007/s00340-022-07768-0
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DOI: https://doi.org/10.1007/s00340-022-07768-0