## 1 Introduction  
With the rapid advancement of autonomous driving and advanced driver assistance systems (ADASs), there is an increasing demand for enhanced environmental perception capabilities of in-vehicle sensing systems under nighttime and low-light conditions. As a crucial component of intelligent driving perception, electronic rearview mirrors capture real-time external scenes through cameras and project them onto in-vehicle displays, effectively overcoming the limited field of view inherent in traditional mirrors. However, complex artificial light sources at night, such as streetlights, oncoming headlights, and illuminated billboards, often generate glare, glow, and flare effects. These bright areas overexpose parts of the image and hide important surrounding details. This loss of visual clarity makes it harder for drivers to see the road and potential hazards accurately, compromising safety. Consequently, effectively suppressing glare interference and recovering details in occluded regions have become a key challenge for ensuring nighttime driving safety and enhancing the reliability of intelligent driving systems. Current solutions for overexposure correction and nighttime image enhancement primarily include traditional methods and deep learning-based approaches. Traditional methods, such as histogram equalization [1] and Retinex-based image enhancement [2], have long been discussed in the classical literature on digital signal processing [3; 4] and digital image processing [5]. However, these methods often result in over-enhancement, loss of details, and unnatural visual appearances, while also struggling to distinguish and suppress localized strong glare regions. Additionally, gamma correction, although capable of improving overall brightness, is ineffective in addressing local overexposure caused by glare. With the development of computer vision [6] and deep learning, methods based on generative adversarial networks (GANs), convolutional neural networks (CNNs), and Transformer architectures have become research hotspots. While effective at enhancing visibility in low-light conditions, these methods lack precise mechanisms to detect and suppress localized glare. As a result, local overexposure remains a challenge, limiting their generalization and robustness in complex nighttime scenarios. Additionally, many of these models are computationally heavy, which further limits their practical deployment. To address these challenges, we improved the network architecture and proposed the Nighttime Road Glare Suppression Network (NRGS-Net), which effectively suppresses glare and restores details while reducing the number of model parameters. Specifically, to handle the diverse glare interference caused by the uncertainty of light source positions and shapes, we designed a gated positional attention (GPA) module, which integrates positional encoding and local contextual information to guide the network in accurately locating and suppressing glare regions, thereby improving the visibility of affected areas. Additionally, we proposed an improved Uformer backbone architecture, wherein the downsampling stage employs an efficient structure combining depthwise separable convolutions with pointwise convolutions, reducing computational load while preserving critical spatial information. The upsampling stage incorporates a residual PixelShuffle module to enable efficient restoration of affected regions. In the Locally Context-Aware Feed-Forward Network (LCA-FFN), we utilize depthwise convolutions to capture local contextual features, combined with Squeeze-and-Excitation (SE) channel attention to adaptively adjust channel-wise feature weights. This design effectively suppresses irrelevant or interfering features, further enhancing the model's capability to recover fine details in glare-affected regions.