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    This study introduces a novel position relation model for Face Super-Resolution (FSR) to reconstruct high-resolution (HR) faces from severely degraded low-resolution (LR) images, even with occlusions. The model effectively compensates for lost facial structures by leveraging relationships between image patches.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Face Super-Resolution (FSR) typically processes images patch-by-patch, struggling with severe degradation and occlusions.
    • Reconstructing high-resolution (HR) faces from low-resolution (LR) images with significant damage remains a challenge.

    Purpose of the Study:

    • To develop an effective patch-level model for FSR that addresses severe degradation and occlusions.
    • To improve the quality of reconstructed HR faces by compensating for lost information.

    Main Methods:

    • Proposed a novel patch-level face model named the position relation model for FSR.
    • This model establishes mapping relationships between face positions based on similarity.
    • It constrains each patch position using global face relationships to find substitute patches for degraded areas.

    Main Results:

    • The position relation model effectively compensates for lost facial structures by utilizing information from remote pixels.
    • Successfully hallucinated LR images with severe degradations (noise, blur, occlusions) into HR ones.
    • Demonstrated superior performance against state-of-the-art methods through quantitative and qualitative evaluations.

    Conclusions:

    • The proposed position relation model significantly enhances Face Super-Resolution for degraded and occluded images.
    • This approach offers a robust solution for reconstructing high-quality facial images from challenging low-resolution inputs.
    • The method shows promise for real-world applications requiring accurate facial reconstruction.