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Improving efficiency of ghost imaging by tapping into the obtained data.

Chen Chang, Long-Kun Du, Shuai Sun

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    |April 12, 2025
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    Summary

    Ghost imaging systems can now achieve higher image quality with fewer samples. A new metric, imaging efficiency, quantifies information acquisition, leading to improved performance through post-processing techniques.

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    Area of Science:

    • Optics and Photonics
    • Information Theory
    • Computational Imaging

    Background:

    • Theoretical ghost imaging suggests quality improves with sampling, but practical limits exist.
    • Maximum achievable image quality and sampling requirements indicate system information acquisition capability.
    • Existing ghost imaging methods do not fully utilize detected information for image reconstruction.

    Purpose of the Study:

    • To introduce a quantitative metric, imaging efficiency, for evaluating information acquisition in ghost imaging systems.
    • To propose methods for improving imaging efficiency by leveraging obtained data.
    • To demonstrate a post-processing technique to enhance ghost imaging performance.

    Main Methods:

    • Definition and calculation of imaging efficiency.
    • Development of a post-processing algorithm to improve imaging efficiency.
    • Experimental validation of the proposed method in a ghost imaging setup.

    Main Results:

    • The proposed imaging efficiency metric effectively quantifies information acquisition capability.
    • The post-processing method significantly improved the maximum achievable image quality.
    • The number of required samplings to reach maximum quality was substantially reduced.

    Conclusions:

    • Imaging efficiency provides a valuable metric for assessing and optimizing ghost imaging systems.
    • Post-processing of acquired data is a viable strategy to enhance ghost imaging performance.
    • The developed method offers a practical approach to improve both image quality and sampling efficiency in ghost imaging.