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    This study introduces a novel method for estimating illuminant color in images with multiple light sources. The approach uses a factor graph and data-driven prototypes for accurate, pixelwise color correction without external libraries or user input.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Photography

    Background:

    • Accurate illuminant color estimation is crucial for image analysis and color correction.
    • Existing methods often rely on libraries or user input, limiting their applicability.
    • Scenes with multiple light sources present a significant challenge for traditional color constancy algorithms.

    Purpose of the Study:

    • To develop a statistically data-driven method for recovering spatially varying illuminant color estimates.
    • To enable pixelwise illuminant color recovery without requiring external libraries or user intervention.
    • To leverage factor graphs and maximum a posteriori inference for robust illuminant estimation.

    Main Methods:

    • Formulating the illuminant recovery problem within a statistically data-driven framework.
    • Utilizing a factor graph defined across the scale space of the input image.
    • Employing a set of data-driven illuminant prototypes and maximum a posteriori inference.

    Main Results:

    • A pixelwise illuminant color estimate that is independent of external libraries or user input.
    • Successful recovery of illuminant estimates using maximum a posteriori inference.
    • Computation of probability marginals via Delaunay triangulation on the factor graph.

    Conclusions:

    • The proposed method effectively recovers spatially varying illuminant colors from complex lighting conditions.
    • The factor graph approach provides a robust and data-driven solution for pixelwise illuminant estimation.
    • Demonstrated utility on standard datasets and real-world images, showing competitive color correction results.