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Underwater Image Enhancement With Hyper-Laplacian Reflectance Priors.

Peixian Zhuang, Jiamin Wu, Fatih Porikli

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 10, 2022
    PubMed
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    This study introduces a new hyper-Laplacian reflectance priors model for underwater image enhancement. It improves visibility and color accuracy by effectively separating and refining reflectance and illumination components.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Underwater images suffer from poor visibility due to light absorption and scattering.
    • Retinex variational models enhance images but struggle with unnatural colors and ambiguous details.

    Purpose of the Study:

    • To propose a novel retinex variational model for superior underwater image enhancement.
    • To address limitations of existing methods regarding detail preservation and color naturalness.

    Main Methods:

    • Developed a hyper-Laplacian reflectance priors model using l1/2-norm on reflectance gradients.
    • Employed l2 norm for accurate illumination estimation.
    • Utilized an alternating minimization algorithm for efficient subproblem optimization.

    Main Results:

    • The proposed model enhances salient structures and fine details while preserving natural colors.
    • Achieved superior performance in subjective and objective assessments compared to existing methods.
    • Demonstrated effectiveness without requiring prior knowledge of underwater conditions.

    Conclusions:

    • The hyper-Laplacian reflectance priors model offers a robust solution for underwater image enhancement.
    • The method effectively overcomes limitations of traditional retinex models for underwater scenes.