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Updated: Jan 15, 2026

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Self-Supervised Unfolding Network With Shared Reflectance Learning for Low-Light Image Enhancement.

Jia Liu, Yu Luo, Guanghui Yue

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 13, 2026
    PubMed
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    This study introduces S2UNet, a self-supervised unfolding network for low-light image enhancement. It overcomes limitations of existing methods by using a novel optimization model and a self-supervised denoising mechanism.

    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Low-light image enhancement (LIE) is crucial for various applications.
    • Existing methods often ignore Retinex theory's physical priors or require paired data.

    Purpose of the Study:

    • To propose a novel self-supervised unfolding network (S2UNet) for low-light image enhancement.
    • To address limitations of existing methods, including data dependency and physical prior modeling.

    Main Methods:

    • Developed a self-supervised unfolding network (S2UNet) based on Retinex theory.
    • Formulated a novel optimization model enforcing content consistency under varying illumination.
    • Employed gamma correction to create illumination-different image pairs for self-supervision.
    • Integrated a self-supervised denoising mechanism to mitigate noise amplification.

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    Main Results:

    • S2UNet demonstrates superior performance over state-of-the-art unsupervised methods.
    • Achieved competitive results compared to supervised methods in quantitative metrics and visual quality.
    • Extensive experiments on nine benchmark datasets validate the proposed approach.

    Conclusions:

    • The proposed S2UNet effectively enhances low-light images using self-supervised learning.
    • The method successfully models physical priors and reduces reliance on paired data.
    • S2UNet offers a robust solution for low-light image enhancement with improved noise suppression.