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Efficient Glare Suppression Network for Nighttime Images with Lightweight Parallel Attention and Ghost Convolution
Ruoyu Yang1, Huaixin Chen1, Sijie Luo1
1School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
This study introduces a lightweight glare suppression network (LGSNet) for nighttime driving. The efficient LGSNet effectively reduces glare and enhances details, offering a practical solution for edge devices.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Image Processing
Background:
- Nighttime road scenes suffer from glare, overexposure, and detail loss from artificial lights.
- Existing glare suppression models are computationally intensive and difficult to deploy on edge devices.
Purpose of the Study:
- To propose a lightweight glare suppression network (LGSNet) for nighttime road scenes.
- To address the limitations of existing models regarding parameter count and computational complexity.
- To enable efficient glare suppression on resource-constrained edge devices.
Main Methods:
- Developed a lightweight glare suppression network (LGSNet) using ghost depthwise separable convolution (GhostDSC) and Lightweight Parallel Attention (LPA).
- Integrated GhostDSC blocks into a U-Net architecture to reduce parameters and computational cost.
- Employed an LPA module to enhance attention to glare regions and details.
- Utilized a joint loss function (background, glare, reconstruction loss) for optimized suppression and preservation.
Main Results:
- The proposed LGSNet achieved competitive performance on public (Flare7K++) and custom (NRGD) datasets.
- LGSNet has significantly fewer parameters (7.45 M) compared to U-Net and Uformer.
- The method demonstrated effectiveness in suppressing glare and restoring details across various metrics (PSNR, SSIM, LPIPS, NIQE, BRISQUE, PIQE).
Conclusions:
- LGSNet offers a superior trade-off between model complexity and performance for glare suppression.
- The network provides an efficient solution for resource-aware glare suppression tasks on edge devices.
- The proposed method effectively mitigates glare interference while preserving crucial scene details.
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