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    This study introduces a novel method for image patch similarity matching by combining small patches with contextual information. This approach enhances matching quality and application performance without requiring larger databases.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Patch similarity is crucial for image analysis tasks.
    • Large patches offer better matching but require exponentially larger databases due to the curse of dimensionality.
    • Small patches are preferred despite limitations, as dense sampling is often not feasible.

    Purpose of the Study:

    • To develop a method that leverages the benefits of large patches while using small patches.
    • To improve image patch matching quality without increasing database size.
    • To enhance performance in image processing applications.

    Main Methods:

    • Proposed method concatenates conventional small patches with a compact representation of their large surroundings (context).
    • Contextual descriptors are computed based on the self-similarity of the patch's surrounding.
    • The dimensionality increase is minimal, adding only a few values to the patch representation.

    Main Results:

    • The proposed method achieves better matches compared to conventional small patches.
    • Performance improvements are demonstrated without the need for increased database size.
    • Effectiveness validated across image denoising, depth super-resolution, and frame-rate up conversion.

    Conclusions:

    • Concatenating small patches with contextual information offers a practical solution for improved image similarity matching.
    • This approach effectively mitigates the curse of dimensionality.
    • The method shows promise for various computer vision applications requiring robust patch matching.