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    Correspondence ghost imaging (CGI) is mathematically linked to machine learning classifiers. Applying linear models like logistic regression improves CGI image quality and enables faster, accurate classification tasks.

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    Area of Science:

    • Computational imaging
    • Machine learning
    • Optical physics

    Background:

    • Correspondence ghost imaging (CGI) is an advanced imaging technique.
    • Machine learning offers novel approaches to enhance image reconstruction and analysis.
    • Understanding the mathematical connections between imaging and classification is crucial.

    Purpose of the Study:

    • To analyze correspondence ghost imaging (CGI) through the lens of machine learning.
    • To investigate the mathematical consistency between CGI and the perceptron model.
    • To improve CGI image quality and computational efficiency using linear classification algorithms.

    Main Methods:

    • Mathematical analysis of CGI and the perceptron model.
    • Application of linear classification algorithms (perceptron, logistic regression) to CGI data.
    • Experimental and simulation-based evaluation of image reconstruction quality (SSIM) and classification performance.

    Main Results:

    • Linear classification algorithms, specifically logistic regression, enhance CGI image reconstruction quality, achieving the highest SSIM.
    • CGI, when interpreted as a lightweight linear classifier, performs standard classification tasks with accuracy comparable to logistic regression.
    • CGI achieves significantly reduced computation time (up to 10x faster) for classification tasks.

    Conclusions:

    • Correspondence ghost imaging demonstrates intrinsic mathematical consistency with linear classifiers.
    • The integration of machine learning, particularly linear models, offers a powerful approach to improve CGI.
    • CGI holds potential as an efficient, lightweight linear classifier for various computational tasks.