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    This study presents a new probabilistic Retinex algorithm using percentile-based white value calculation for improved image processing. The novel approach offers both global and local processing versions with varying computational demands.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Photography

    Background:

    • Retinex algorithms are widely used for image enhancement by adjusting pixel lightness based on a reference white value.
    • Existing Retinex methods employ diverse strategies for determining this reference white value, impacting spatial property analysis.
    • A need exists for novel Retinex formalizations that explore alternative methods for reference white value computation.

    Purpose of the Study:

    • To introduce a novel probabilistic version of the Retinex algorithm.
    • To investigate the spatial properties of this probabilistic Retinex model.
    • To propose an alternative procedure for computing the reference white value using pixel population percentiles.

    Main Methods:

    • Probabilistic formalization of the random spray Retinex sampling.
    • Development of two algorithm versions: global and local behavior.
    • Computation of reference white values from percentile values of the pixel population.

    Main Results:

    • Introduction of a probabilistic Retinex model enhancing spatial property investigation.
    • Successful implementation of percentile-based reference white value computation.
    • Characterization of global and local Retinex versions with distinct computational costs.

    Conclusions:

    • The proposed probabilistic Retinex offers a new perspective on image processing and spatial analysis.
    • Percentile-based reference white value calculation provides an effective alternative to existing methods.
    • The developed global and local versions cater to different computational requirements in image analysis.