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Fast ACE (FACE): An Error-Bounded Approximation of Automatic Color Equalization.

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    We developed an efficient algorithm for approximating Automatic Color Equalization (ACE) in images. This method offers speedups and high accuracy, with lower approximation errors than current techniques.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Algorithm Optimization

    Background:

    • Automatic Color Equalization (ACE) is crucial for image enhancement.
    • Standard ACE computations can be computationally intensive, often quadratic.
    • Existing approximation methods may lack sufficient accuracy or efficiency.

    Purpose of the Study:

    • To present an efficient algorithm for approximating ACE.
    • To provide an upper-bound on the approximation error.
    • To improve computational complexity compared to standard ACE.

    Main Methods:

    • Utilized Summed Area Tables for efficient computation.
    • Implemented a plane partitioning strategy into rectangular regions.
    • Achieved pseudo-linear asymptotic complexity relative to pixel count.

    Main Results:

    • Demonstrated significant speedups in ACE computation.
    • Achieved high accuracy in color equalization.
    • Reported lower approximation errors than existing methods.

    Conclusions:

    • The proposed algorithm offers an efficient and accurate ACE approximation.
    • The method's pseudo-linear complexity makes it scalable for large images.
    • A publicly available reference implementation is provided for accessibility.