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Fast Adaptive Bilateral Filtering.

Ruturaj G Gavaskar, Kunal N Chaudhury

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    |September 21, 2018
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    Summary
    This summary is machine-generated.

    This study introduces a fast algorithm for adaptive bilateral filtering, significantly speeding up image processing tasks like sharpening and artifact removal. The new method achieves substantial acceleration without compromising visual quality.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Signal Processing

    Background:

    • Classical bilateral filters use fixed Gaussian kernels for edge-preserving smoothing.
    • Adaptive bilateral filters offer enhanced flexibility but demand intensive computation.
    • Existing fast algorithms for bilateral filtering typically require a fixed range kernel.

    Purpose of the Study:

    • To develop a computationally efficient algorithm for adaptive bilateral filtering.
    • To address the limitations of existing fast algorithms that do not support adaptive range kernels.
    • To accelerate adaptive bilateral filtering without sacrificing image quality.

    Main Methods:

    • Proposed a novel algorithm for adaptive bilateral filtering.
    • Utilized local histograms in range space for filtering operations.
    • Approximated the filter using analytic functions derived from polynomial fitting and integration-by-parts.
    • Employed fast convolutions for moment matching and recursive computation for analytic functions.

    Main Results:

    • Achieved a significant acceleration of adaptive bilateral filtering compared to brute-force implementation.
    • The algorithm's complexity is independent of the spatial filter width.
    • Demonstrated effectiveness in applications including sharpening, JPEG deblocking, and texture filtering.
    • Maintained visual quality without perceptible distortions.

    Conclusions:

    • The proposed fast algorithm enables efficient adaptive bilateral filtering.
    • This method overcomes the computational bottleneck of adaptive bilateral filtering.
    • The approach is versatile and effective for various image processing tasks.