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Updated: Jun 4, 2026

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Published on: August 17, 2011

Content-illumination coupling guided low-light image enhancement network.

Ruini Zhao¹, Meilin Xie^1,2, Xubin Feng³

¹Key Laboratory of Space Precision Measurement Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xian, 710119, China.

Scientific Reports

|April 11, 2024

Summary

This study introduces CICGNet, a novel low-light image enhancement network that effectively suppresses noise and avoids distortions. CICGNet improves image quality by coupling content and illumination, outperforming existing methods.

Keywords:

End-to-end Low-light enhancement Pre- and post-activation Retinex Truss topology

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Last Updated: Jun 4, 2026

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Existing low-light enhancement methods struggle with noise suppression, structural distortion, and color artifacts.
Halos and artifacts often arise from current enhancement techniques, degrading image quality.

Purpose of the Study:

To propose a novel content-illumination coupling guided low-light image enhancement network (CICGNet).
To address limitations of current algorithms by preventing noise amplification and structural/color distortions.

Main Methods:

Developed CICGNet utilizing a truss topology based on Retinex for end-to-end image decomposition.
Preserved content features and enhanced illumination features through depth and width processing.
Prevented illumination misestimation using pre- and post-activation features to avoid halos and artifacts.

Main Results:

CICGNet demonstrated superior performance compared to advanced attention-based and state-of-the-art restoration algorithms.
Ablation studies confirmed the effectiveness of the proposed low-light enhancement approach.
The study highlighted the positive impact of CICGNet on downstream computer vision tasks.

Conclusions:

CICGNet effectively enhances low-light images by decoupling content and illumination, mitigating noise and distortions.
The proposed network offers a significant advancement in low-light image enhancement and computer vision applications.