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JDPNet: A Network Based on Joint Degradation Processing for Underwater Image Enhancement.

Tao Ye, Hongbin Ren, Chongbing Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 12, 2026
    PubMed
    Summary

    This study introduces JDPNet, a novel network for joint degradation processing in underwater images. JDPNet effectively handles complex, coupled degradations, improving image quality and offering a superior balance of performance and efficiency.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Underwater Imaging

    Background:

    • Underwater images suffer from complex, non-linearly coupled degradations due to water's properties.
    • Existing methods often address degradations individually, neglecting their coupled nature.
    • Processing these coupled degradations effectively is a significant challenge in computer vision.

    Purpose of the Study:

    • To develop a unified framework for processing multiple, coupled underwater image degradations.
    • To effectively mine and unify information from coupled degradation features.
    • To improve the quality of underwater images by addressing nonlinear interactions.

    Main Methods:

    • Proposed JDPNet (Joint Degradation Processing Network) with a joint feature-mining module.
    • Introduced a probabilistic bootstrap distribution strategy for feature adjustment.
    • Developed AquaBalanceLoss to balance color, clarity, and contrast during training.

    Main Results:

    • JDPNet demonstrated state-of-the-art performance on multiple underwater image datasets.
    • The network effectively captured and processed nonlinear interactions of coupled degradations.
    • Achieved a favorable tradeoff between performance, parameter size, and computational cost.

    Conclusions:

    • JDPNet offers a robust solution for joint underwater image degradation processing.
    • The proposed methods successfully address the limitations of existing approaches.
    • This work advances the field of underwater image restoration with an efficient and effective network.