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    This study presents a new method to remove haze and enhance image contrast in outdoor and underwater scenes. The approach uses patch similarity to estimate depth and reduce atmospheric scattering for clearer images.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Remote Sensing

    Background:

    • Atmospheric scattering degrades outdoor scene visibility, causing light attenuation and haze.
    • Poor illumination exacerbates visibility issues.
    • Underwater environments introduce wavelength-dependent attenuation and color cast.

    Purpose of the Study:

    • To generalize and reformulate local haziness for suppressing atmospheric/underwater effects.
    • To enhance image contrast in degraded visual conditions.
    • To develop a method effective across diverse lighting and environments (day, night, underwater).

    Main Methods:

    • Estimating parameters by analyzing patch similarity within local neighborhoods.
    • Assuming gradual depth changes in local neighborhoods, approximated by patch similarity.
    • Deducing additive haze using non-local mean of patches and estimating attenuation coefficients from patch similarity weights.

    Main Results:

    • Successfully reduced haze components in images from various conditions.
    • Significantly enhanced image contrast.
    • Demonstrated effectiveness in daytime, nighttime, and underwater scenarios.

    Conclusions:

    • The proposed method effectively suppresses haze and enhances image quality.
    • Patch similarity is a viable approach for estimating scene depth and atmospheric effects.
    • The technique offers a generalized solution for image restoration in challenging visual environments.