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Interactive removal and ground truth for difficult shadow scenes.

Han Gong, Darren Cosker

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
    |September 9, 2016
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    Summary
    This summary is machine-generated.

    A new user-guided shadow removal algorithm offers fast, high-quality results, even for difficult shadows. This method, validated on a diverse dataset, outperforms existing techniques for image shadow removal.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Shadows in images present significant challenges for automated analysis and visual quality.
    • Existing shadow removal techniques often struggle with complex shadow types like textured or colored shadows.

    Purpose of the Study:

    • To develop a user-centric, robust, and high-quality shadow removal algorithm.
    • To introduce a validated, multi-scene ground truth dataset for shadow removal research.
    • To establish an online benchmark for comparing shadow removal methods.

    Main Methods:

    • An on-the-fly learning approach for shadow detection using user-guided inputs.
    • Penumbra registration to a normalized frame for accurate illumination estimation and removal.
    • Creation of a 186-image dataset covering diverse shadow types (soft, textured, colored, broken).

    Main Results:

    • The proposed algorithm demonstrates superior performance in shadow detection and removal across various shadow categories.
    • The new dataset enables the most comprehensive comparison of state-of-the-art shadow removal methods to date.
    • The online benchmark facilitates future open comparisons and advancements in the field.

    Conclusions:

    • The user-centric method provides an effective solution for high-quality shadow removal.
    • The validated dataset and benchmark website significantly contribute to advancing shadow removal research.