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Attention-guided low-light image enhancement only via dark instances
Ruiyao Wang1,2, Siti Norbaya Daud3, Hong Li4
1Henan International Joint Laboratory of Machine Vision and Intelligent Systems, Department of Information Engineering, Pingdingshan University, Pingdingshan, 467000, Henan, China. 2546@pdsu.edu.cn.
Scientific Reports
|June 30, 2026
Summary
This study introduces UALIE, an effective low-light image enhancement method. It improves image quality using Retinex theory and attention mechanisms without needing reference images.
Area of Science:
- Computer Vision
- Image Processing
Background:
- Low-light conditions degrade image quality, impacting computer vision tasks.
- Existing methods often require extensive labeled data or reference images.
Purpose of the Study:
- To propose a novel and effective low-light image enhancement (LLIE) method.
- To reduce the dependency on paired normal-light reference images during training.
Main Methods:
- Utilizes Retinex theory to decompose images into illumination and reflectance components.
- Employs a U-Net inspired architecture for multi-level feature extraction.
- Incorporates an attention mechanism to focus on salient image regions.
Main Results:
- UALIE effectively enhances low-light images.
- The method achieves superior performance compared to state-of-the-art techniques.
- Demonstrated effectiveness on multiple public datasets.
Conclusions:
- UALIE offers a simple yet effective solution for low-light image enhancement.
- The proposed method significantly reduces the need for labeled data.
- UALIE shows strong potential for practical computer vision applications.
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