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Aesthetics-Guided Low-Light Enhancement.

Dong Liang, Yuanhang Gao, Ling Li

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    Summary
    This summary is machine-generated.

    This study introduces aesthetics-guided low-light image enhancement (ALL-E) to improve image quality by incorporating human preferences. The ALL-E+ model enhances and denoises images, outperforming existing methods in subjective and objective evaluations.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Low-light image enhancement (LLE) performance is subjective, necessitating human preference integration.
    • Current LLE methods lack consideration for aesthetic qualities, using potentially inadequate heuristic criteria.

    Purpose of the Study:

    • Introduce a novel paradigm, aesthetics-guided low-light image enhancement (ALL-E), incorporating human aesthetic preferences.
    • Develop a reinforcement learning framework with an aesthetic reward for training LLE models.
    • Present ALL-E+, a unified framework for sequential enhancement and denoising.

    Main Methods:

    • Proposed the aesthetics-guided low-light image enhancement (ALL-E) paradigm.
    • Utilized a reinforcement learning framework with an aesthetic reward, where pixels act as agents.
    • Developed ALL-E+, a two-stage approach for enhancement and denoising.

    Main Results:

    • ALL-E+ demonstrated significant improvements in both subjective visual experience and objective evaluation.
    • Integrating aesthetic preferences enhanced the visual quality of the processed images.
    • The proposed methods outperformed state-of-the-art LLE techniques on various benchmarks.

    Conclusions:

    • Aesthetics-guided LLE is crucial for improving subjective visual quality.
    • The ALL-E and ALL-E+ frameworks offer a robust approach to LLE and denoising.
    • The proposed methods represent a significant advancement over existing low-light image enhancement techniques.