## 2 Related Work  
### Traditional Methods  
In terms of traditional methods, [PERSON] et al. [7] proposed an automatic detection and removal method for glare artifacts caused by bright light sources in images captured by cameras. This method utilizes geometric, morphological, luminance, and chromatic features for glare detection, combined with the optical characteristics of the lens system and filters to screen candidate glare regions. By defining confidence measures, the method adaptively selects the true glare regions and generates glare masks, which are then used with sample-based inpainting methods to reconstruct the affected areas, achieving effective and automatic glare removal in images. Subsequently, [PERSON] et al. [8] introduced a single-image glare removal method based on image decomposition, further enhancing glare suppression and image quality. Specifically, the method decomposes the glare image into scene and glare layers, corrects brightness attenuation and color deviation, and employs a local standard deviation-based contrast enhancement algorithm to restore details and contrast, addressing color distortion and contrast reduction problems typically induced under strong light sources. To address the quality degradation in coal mine surveillance videos caused by fogging, low illumination, and glare, [PERSON] et al. [9] proposed a hybrid enhancement algorithm (SSR-BF) that combines single-scale Retinex (SSR) with bilateral filtering (BF). By incorporating BF within the SSR framework, the method reduces noise and preserves edge details, thereby enhancing the quality of coal mine surveillance images. Additionally, [PERSON] et al. [10] proposed a region-based local image enhancement method, which divides each frame into three independent regions and applies a dynamic patch-based low-light vision enhancement strategy, adapting patch sizes according to pixel brightness. This approach effectively suppresses over-enhancement in the sky and dark regions while emphasizing the brightness of roads and surrounding areas. By introducing state judgment to avoid frequent atmospheric light calculations and using local Gaussian filtering along regional boundaries, the method reduces computational time and improves efficiency. In terms of adaptive enhancement, [PERSON] et al. [11] proposed an adaptive gamma correction method that dynamically adjusts gamma values to enhance image contrast, thereby improving adaptability to various lighting conditions. [PERSON] et al. [12] proposed a minimum mean brightness error bi-histogram equalization method, aiming to enhance contrast while better preserving image details. [PERSON] et al. [13] built upon the dark channel prior algorithm by integrating denoising and dehazing techniques to effectively mitigate halo artifacts in images. In summary, traditional methods for glare suppression in low-light images have achieved certain levels of success in improving image quality. However, these methods still face challenges such as reliance on physical or rule-based modeling, limited adaptability, overall brightness reduction, halo issues, and high computational complexity, which constrain their further application in complex and dynamic scenes.