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Updated: Mar 14, 2026

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Enhancing Underwater Images via Resonant Fusion.

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    This summary is machine-generated.

    This study introduces the Resonant Fusion (ReFu) framework for superior underwater image enhancement. ReFu effectively restores details and structures by fusing spatial and frequency domain information, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Underwater image enhancement faces challenges due to scene complexity.
    • Existing methods struggle with simultaneous detail and layout restoration.

    Purpose of the Study:

    • To develop a novel framework for improved underwater image enhancement.
    • To address limitations in preserving structural details and global layouts.

    Main Methods:

    • Proposed a Resonant Fusion (ReFu) framework.
    • Designed frequency and spatial decomposers for cross-domain cue capture.
    • Introduced a resonant fuser for adaptive integration of high- and low-frequency information.

    Main Results:

    • ReFu significantly enhances structural preservation and detail refinement.
    • Generated visually natural and perceptually friendly underwater images.
    • Consistently outperformed state-of-the-art methods on diverse benchmarks.

    Conclusions:

    • The ReFu framework effectively leverages cross-domain information for superior underwater image enhancement.
    • Ablation studies confirm the efficacy and necessity of the ReFu mechanism.