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Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective.

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    This study introduces a novel optimizer for image super-resolution (SR) that balances perceptual quality and distortion. By combining evolutionary algorithms with Adam, it achieves superior results compared to existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Achieving high perceptual quality and low distortion in super-resolution (SR) is challenging.
    • Existing SR methods struggle to balance conflicting objectives like perceptual and reconstruction losses.
    • Gradient-based optimizers like Adam face difficulties with contradictory loss functions.

    Purpose of the Study:

    • To address the perception-distortion trade-off in image super-resolution.
    • To develop a novel optimization method that effectively balances competing objectives.
    • To improve both perceptual quality and reconstruction fidelity in SR models.

    Main Methods:

    • Formulated the perception-distortion trade-off as a multi-objective optimization problem.
    • Developed a hybrid optimizer integrating a gradient-free evolutionary algorithm (EA) with gradient-based Adam.
    • Designed a fusion network to merge an EA-Adam-generated population of models.

    Main Results:

    • The proposed EA-Adam optimizer effectively balances perception and distortion in SR.
    • A population of optimal models with diverse perception-distortion preferences was obtained.
    • The fusion network successfully merged models, enhancing the perception-distortion trade-off.
    • Experimental results show improved perceptual quality and reconstruction fidelity compared to competitors.

    Conclusions:

    • The novel EA-Adam optimizer offers a superior approach to balancing perception and distortion in image super-resolution.
    • The developed fusion network effectively consolidates diverse model strengths for enhanced SR performance.
    • This method provides a promising direction for future research in image restoration tasks.