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Reference-Based Image and Video Super-Resolution via C2-Matching.

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    Summary
    This summary is machine-generated.

    This study introduces C²-Matching for reference-based super-resolution, enabling robust image enhancement across transformations and resolutions. The method significantly improves low-resolution image and video quality by explicitly matching features between input and reference images.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Reference-based Super-Resolution (Ref-SR) enhances low-resolution (LR) images using high-resolution (HR) references.
    • Existing methods struggle with transformation (scale, rotation) and resolution gaps between images.
    • Implicit correspondence matching limits texture borrowing for information loss compensation.

    Purpose of the Study:

    • To propose C²-Matching, a novel approach for explicit and robust matching across transformation and resolution gaps in Ref-SR.
    • To improve the accuracy and robustness of super-resolution techniques.
    • To introduce a new dataset (WR-SR) for evaluating Ref-SR in realistic scenarios.

    Main Methods:

    • A contrastive correspondence network learns transformation-robust correspondences using augmented input views.
    • Teacher-student correlation distillation bridges the resolution gap by guiding LR-HR matching with HR-HR matching.
    • A dynamic aggregation module addresses potential misalignment between input and reference images.

    Main Results:

    • C²-Matching significantly outperforms state-of-the-art methods by up to 0.7 dB on the CUFED5 benchmark.
    • The method demonstrates improved performance in Reference-based Video Super-Resolution (Ref VSR).
    • C²-Matching shows excellent generalizability on the WR-SR dataset and robustness to scale and rotation transformations.

    Conclusions:

    • C²-Matching offers a robust solution for Ref-SR challenges, effectively handling transformation and resolution discrepancies.
    • The proposed method advances the capabilities of both image and video super-resolution.
    • The WR-SR dataset facilitates more realistic evaluation of Ref-SR algorithms.