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    This study introduces a flexible cascaded framework for underwater image enhancement (UIE), effectively addressing multiple degradations. The novel approach combines general image enhancement with adaptive color correction for superior visual quality.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Underwater Imaging

    Background:

    • Underwater images suffer from severe degradation like color cast and haziness due to light absorption and scattering.
    • Existing methods struggle with multiple, coexisting degradations common in underwater scenes.
    • Degradations are categorized into medium-agnostic (e.g., low-light) and medium-specific (e.g., color distortion).

    Purpose of the Study:

    • To develop a robust framework for enhancing degraded underwater images.
    • To address the challenge of simultaneous multiple degradations in underwater imagery.
    • To improve the quality and usability of underwater images for various applications.

    Main Methods:

    • A cascaded, multi-module underwater image enhancement (UIE) framework is proposed.
    • The framework integrates an in-air image enhancement module for medium-agnostic issues.
    • A novel Adaptive Color Channel Compensation Network (AC3Net) is introduced for medium-specific color distortion.

    Main Results:

    • The proposed framework effectively enhances diverse degraded underwater images.
    • The cascaded approach demonstrates flexibility by integrating different enhancement modules.
    • Validated effectiveness across various underwater visual perception tasks.

    Conclusions:

    • The cascaded UIE framework successfully tackles complex, coexisting degradations.
    • The AC3Net component specifically addresses water-induced color distortions.
    • The framework offers a versatile solution for improving underwater image quality.