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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Guided Image Filter (GIF) is a local filter known for edge preservation and efficiency.
    • GIFs can produce halo artifacts due to limitations in representing images near edges with a local linear model.

    Purpose of the Study:

    • To propose a gradient domain Guided Image Filter (GIF) that overcomes the halo artifact issue.
    • To enhance edge preservation and overall image quality in various applications.

    Main Methods:

    • Incorporated an explicit first-order edge-aware constraint into the GIF framework.
    • Developed a gradient domain GIF by integrating this constraint.

    Main Results:

    • The proposed gradient domain GIF significantly reduces halo artifacts compared to the original GIF.
    • Demonstrated improved performance in single-image detail enhancement, high dynamic range (HDR) tone mapping, and image saliency detection.
    • Experimental results and theoretical analysis confirm the filter's effectiveness, particularly around edges.

    Conclusions:

    • The gradient domain GIF offers superior image quality, especially in preserving details and minimizing artifacts near edges.
    • The proposed method provides a more robust and effective solution for image enhancement and analysis tasks.