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GRASS: A Gradient-Based Random Sampling Scheme for Milano Retinex.

Michela Lecca, Alessandro Rizzi, Raul Paolo Serapioni

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 31, 2017
    PubMed
    Summary
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    A new Gradient-based Random Sampling Scheme improves color sensation estimation in digital images. This method offers lower computational complexity than Energy-Driven Termite Retinex while maintaining similar performance.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Color Science

    Background:

    • Retinex theory estimates human color sensation from images.
    • Existing Retinex methods use random paths to find local reference white.
    • Energy-Driven Termite Retinex (ETR) uses image-aware paths over high-gradient pixels.

    Purpose of the Study:

    • Introduce a novel Gradient-based Random Sampling Scheme (GRASS).
    • Reduce computational complexity of image-aware sampling in Retinex algorithms.
    • Maintain or improve the performance of color sensation estimation.

    Main Methods:

    • Developed a new sampling scheme inspired by ETR's image-aware principles.
    • Focused on gradient magnitudes for path selection to reduce complexity.

    Related Experiment Videos

  • Evaluated the scheme's performance and computational cost.

    • Main Results:

    • The proposed GRASS achieves similar performance to ETR.
    • GRASS demonstrates significantly lower computational complexity.
    • The sampling scheme can be viewed as both path-based and 2D sampling.

    Conclusions:

    • GRASS offers an efficient alternative for Retinex-based color sensation estimation.
    • The method effectively balances performance and computational cost.
    • GRASS provides a flexible sampling approach for image processing.