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Auto white balance by surface reflection decomposition.

Ying-Yi Li, Hsien-Che Lee

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
    |October 17, 2017
    PubMed
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    This study introduces a novel auto white balance (AWB) method using surface reflection decomposition. This robust approach improves illuminant chromaticity estimation by canceling specular components, outperforming existing algorithms.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Color Science

    Background:

    • Auto white balance (AWB) is crucial for accurate color reproduction in imaging.
    • Current AWB algorithms often rely on scene statistics, making them sensitive to content variations.
    • A need exists for robust AWB methods insensitive to scene content.

    Purpose of the Study:

    • To develop a more robust auto white balance (AWB) algorithm.
    • To propose new physical features for AWB based on surface reflection decomposition.
    • To improve illuminant chromaticity estimation by effectively canceling specular reflection components.

    Main Methods:

    • Surface reflection decomposition into specular and diffuse components.
    • Estimating illuminant chromaticity by identifying light sources that minimize specular components.

    Related Experiment Videos

  • Formulating two algorithms: minimum projected area and minimum total variation for chromaticity estimation.

    • Main Results:

    • The proposed methods demonstrate improved robustness compared to statistical AWB algorithms.
    • Specular component cancellation proves effective in estimating scene-illuminant chromaticity.
    • Both minimum projected area and minimum total variation algorithms yield favorable results.

    Conclusions:

    • Surface reflection decomposition offers a robust foundation for advanced AWB algorithms.
    • The proposed methods provide a significant improvement in AWB accuracy and stability.
    • This work advances the field of color imaging by enhancing illuminant estimation techniques.