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    This study introduces polarization-based computational ghost imaging (PCGI) for clearer images in fog. PCGI enhances imaging contrast and efficiency by leveraging polarization, outperforming traditional methods.

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    Area of Science:

    • Optics and Photonics
    • Computational Imaging
    • Remote Sensing

    Background:

    • Traditional imaging struggles in foggy environments due to light scattering.
    • Computational ghost imaging (CGI) offers an alternative by using preset patterns and single-pixel detection.
    • Existing CGI methods face challenges in low-contrast scenarios and efficiency.

    Purpose of the Study:

    • To propose and construct a reflective polarization-based computational ghost imaging (PCGI) system for foggy conditions.
    • To quantitatively analyze the imaging performance of PCGI at various optical distances.
    • To enhance imaging efficiency and quality in challenging environments.

    Main Methods:

    • Development of a reflective PCGI system based on a Monte Carlo model.
    • Quantitative investigation of PCGI performance under different optical distances in foggy environments.
    • Proposal of a scanning-mode PCGI (SPCGI) to improve imaging efficiency.

    Main Results:

    • PCGI effectively improves imaging contrast by utilizing polarization differences between targets and background, mitigating scattering interference.
    • Performance analysis shows PCGI's viability across different optical distances in fog.
    • The proposed SPCGI demonstrates significant improvements in both imaging efficiency and quality.

    Conclusions:

    • Polarization-based CGI is a promising technique for improving imaging in foggy conditions.
    • Leveraging polarization characteristics is crucial for enhancing contrast and removing scattering.
    • The developed PCGI and SPCGI systems offer advanced solutions for challenging imaging applications.