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Single pixel structured imaging through fog.

Mark Bashkansky, Samuel D Park, John Reintjes

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    This study shows how to use a single-pixel camera and high-pass filters to improve imaging through fog. Compressive sensing yielded better image quality compared to computational ghost imaging for fog penetration.

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    Area of Science:

    • Optics
    • Image Processing
    • Remote Sensing

    Background:

    • Imaging through scattering media like fog presents significant challenges.
    • Temporal variations in fog density degrade image quality and reconstruction.
    • Traditional imaging techniques struggle with low visibility conditions.

    Purpose of the Study:

    • To apply structured imaging with a single-pixel camera for effective fog penetration.
    • To investigate the use of high-pass filters to mitigate fog density fluctuations.
    • To compare image reconstruction techniques for enhanced visibility.

    Main Methods:

    • Utilized a single-pixel camera for structured imaging.
    • Applied high-pass filters to bucket signals to suppress temporal fog variations.
    • Employed computational ghost imaging and compressive sensing for image reconstruction.
    • Quantitatively analyzed and compared various high-pass filters.

    Main Results:

    • High-pass filtering effectively suppressed temporal fog density effects.
    • Both computational ghost imaging and compressive sensing reconstructed images through fog.
    • Compressive sensing demonstrated superior reconstructed image quality.

    Conclusions:

    • Single-pixel camera imaging combined with high-pass filtering is a viable method for fog penetration.
    • Compressive sensing offers improved performance for image reconstruction in foggy conditions.
    • This technique has potential applications in low-visibility remote sensing and autonomous navigation.