# GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception
## 1 Introduction  
In a low-light environment, light intensity is not sufficient. Additionally, the presence of vehicle high beams, street lights, illuminators, and other light sources can create glare, causing an image to appear stratified due to differences in luminance [1]. This results in some regions of the image being over-illuminated while the surrounding regions have very low brightness. As a result, the visibility of details and local contrast in these regions is affected. In real-life scenarios, the glare produced by high-intensity headlights of incoming vehicles during night-time encounters [2] can easily cause discomfort to the driver and interfere with their judgment. Therefore, it is crucial to enhance image quality and utilize embedded device hardware to effectively suppress glare in real time, ensuring the algorithm's practicality in real-world scenarios. Various image enhancement algorithms have been proposed by researchers worldwide to improve image visibility in low-light environments. Each algorithm has its own implementation and varying effects. Notably, [PERSON] et al. [3] have employed frame region segmentation combined with localized enhancement techniques, effectively highlighting enhancement information. However, while their approach achieves good overall visual enhancement and glare suppression, it involves more complex image processing. In the field of night-time low-light image defogging, several methods have been proposed to suppress the glow caused by haze or fog particles. Among them, [PERSON] et al. [4] utilizedluminescent color correction to address the issue of glow elimination during foggy nights. [PERSON] et al. (2015); [PERSON] et al. (2015) proposed a unified variational retina model for haze and glow removal. [PERSON] et al. (2015) eliminated haze and glow through the use of multi-scale saliency detection algorithms, as well as the fusion of ambient light and transmittance. [PERSON] and colleagues (2016) have developed a semi-supervised approach using a generative adversarial network model for hazy atmospheric imaging to tackle foggy night glow. However, these techniques are effective in reducing haze or glow in foggy conditions but do not address glare removal in low-light images. Deep learning-based methods ([PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2018) for low-light image enhancement typically rely on supervised learning, where a large number of paired low/normal light image samples are required. Unsupervised methods ([PERSON] et al., 2017; [PERSON] et al., 2018), on the other hand, use unpaired low/normal light images and employ adversarial training techniques. Semi-supervised methods ([PERSON] et al., 2017; [PERSON] et al., 2018) use unlabeled samples with the help of unpaired high-quality images. In recent developments, zero-reference learning methods have been proposed for low-light enhancement ([PERSON] et al., 2017; [PERSON] et al., 2018). However, most existing methods fail to effectively suppress glare while enhancing low-light regions in night-time images. To address this, [PERSON] et al. (2018) have proposed an unsupervised method that integrates a layer decomposition network and a light effect suppression network. This approach achieves both glare suppression and low-light enhancement without the need for image pairs while preserving image details. High-dynamic-range (HDR) imaging methods can improve low-light images and counteract overexposure. [PERSON] et al. (2018) have explained that their method linearizes a single input night image and splits it into low-frequency (LF) and high-frequency (HF) feature maps. The processed HF feature maps are then combined with the LF feature maps to produce an output map with suppressed glare and improved dark lighting. By utilizing high-dynamic-range (HDR) imaging, the darker regions of the image are greatly improved while the brighter regions are reduced, resulting in an overall improvement in contrast.