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    This study introduces a new image restoration method using structural similarity (SS) for better patch comparison. This novel technique improves upon traditional intensity-based methods, especially for textured images.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Applied Mathematics

    Background:

    • Traditional image restoration often relies on intensity-based patch comparisons, which can be insufficient for images with complex textures or structures.
    • Existing nonlocal filtering methods may struggle with accurately identifying similar image patches based solely on pixel values.

    Purpose of the Study:

    • To propose and develop a novel nonlocal variational technique for image restoration.
    • To leverage structural similarity (SS) information for more effective image patch comparison.
    • To introduce and analyze SS-based nonlocal regularization models for improved image restoration.

    Main Methods:

    • Development of a nonlocal variational technique incorporating structural similarity (SS) information.
    • Formulation of two SS-based nonlocal regularizing functions: SS-based nonlocal quadratic function (SS-NLH1) and SS-based nonlocal total variation function (SS-NLTV).
    • Numerical solution of the proposed variational models using iterative algorithms and analysis of their convergence properties.

    Main Results:

    • Demonstration of the effectiveness of the proposed SS-NLH1 and SS-NLTV models through experimental results.
    • Validation that the SS-based approach outperforms traditional intensity-based methods, particularly for images with texture and structural information.
    • Confirmation of the convergence of the iterative algorithms used to solve the variational models.

    Conclusions:

    • The proposed novel nonlocal variational technique based on structural similarity significantly enhances image restoration quality.
    • SS-based regularization models offer a more robust approach compared to intensity-based methods for diverse image types.
    • The developed iterative algorithms provide an effective numerical solution for the proposed advanced image restoration models.