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Polarization-guided diffusion model for physically inspired underwater image descattering.

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    This study introduces a novel diffusion model and polarization technique to remove scattering in underwater images. The method enhances image clarity and reliability for underwater vision tasks.

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    Area of Science:

    • Computer Vision
    • Optical Engineering
    • Image Processing

    Background:

    • Underwater imaging faces challenges due to light scattering by water, blurring details and degrading vision task reliability.
    • Existing descattering methods often fail in complex scenes due to limited polarization analysis or assumptions.
    • Polarization differences between backscattered and object-reflected light offer a physical cue for scattering removal.

    Purpose of the Study:

    • To develop an advanced underwater image enhancement network.
    • To improve the accuracy and effectiveness of scattering removal in complex underwater environments.
    • To provide high-quality, reliable inputs for downstream underwater vision applications.

    Main Methods:

    • A two-stage approach: descattering prior generation and scattering removal.
    • Utilizing a diffusion model for the first time to extract polarization information for high-quality priors.
    • Incorporating an illumination estimation module to address water absorption and ensure global consistency.

    Main Results:

    • The proposed network achieves state-of-the-art quantitative and qualitative performance in underwater image enhancement.
    • The diffusion model effectively mines polarization data for accurate scattering suppression.
    • The illumination module enhances perceptual naturalness by compensating for absorption.

    Conclusions:

    • The diffusion model and polarization-driven approach significantly improves underwater image quality.
    • This method offers a robust solution for scattering removal in diverse underwater scenes.
    • The enhanced images are suitable for critical downstream computer vision tasks.