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