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GREAT: a gradient-based color-sampling scheme for Retinex.

Michela Lecca, Alessandro Rizzi, Raul Paolo Serapioni

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
    |April 5, 2017
    PubMed
    Summary
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    GREAT, a new noise-free image enhancement method, uses edge information for spatial color sampling. This approach improves brightness, contrast, and dynamic range in images.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Photography

    Background:

    • Accurate modeling of local color spatial distribution is essential for Retinex algorithms.
    • Existing Retinex implementations can be sensitive to noise, impacting performance.

    Purpose of the Study:

    • To introduce GREAT (Gradient RElevAnce for ReTinex), a novel, noise-free implementation of the Milano Retinex algorithm.
    • To enhance image quality by improving brightness, contrast, and dynamic range through an image-aware spatial color sampling method.

    Main Methods:

    • GREAT computes a 2D edge map for each color channel based on a gradient magnitude threshold.
    • Pixel intensities are re-scaled using a weighted average of relevant edge pixel intensities.
    • The selection of relevant edges is guided by their position, gradient magnitude, and intensity relative to the target pixel.

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    Main Results:

    • GREAT successfully implements a noise-free Milano Retinex algorithm.
    • The method effectively enhances image brightness, contrast, and dynamic range.
    • The approach leverages the significance of edges in human color perception for image filtering.

    Conclusions:

    • GREAT offers a robust and noise-free solution for image enhancement within the Milano Retinex framework.
    • The gradient relevance-based edge selection provides an effective mechanism for spatial color sampling.
    • This method holds potential for applications requiring high-quality image processing and color correction.