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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

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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.