## 2 Related Work
### Deep Learning Methods  
In recent years, deep learning methods have achieved remarkable progress in low-light image enhancement and glare removal [14; 15; 16]. To address the limitations of traditional methods in adaptability, detail preservation, and handling of complex scenes, researchers have proposed various efficient and generalizable deep network architectures. In Retinex theory-driven image enhancement, [PERSON] et al. [17] proposed URetinex, which leverages deep learning to replace handcrafted priors used in traditional methods, thereby improving decomposition efficiency. [PERSON] et al. [18] designed a lightweight network, RUAS, which adopts a collaborative no-reference learning strategy to enable fast low-light image enhancement. However, merely increasing brightness often amplifies hidden artifacts, degrading image quality. To address this issue, [PERSON] et al. [19] proposed a decomposition network that separates the input image into illumination and reflectance components, effectively suppressing artifacts while further improving enhancement quality. In the domain of unsupervised image enhancement, [PERSON] et al. [20] introduced EnlightenGAN, which enables image enhancement without requiring paired training data, while [PERSON] et al. [21] proposed a lightweight Transformer-based enhancement model that achieves efficient enhancement with low computational costs. For multimodal fusion and exposure correction, [PERSON] and [PERSON] et al. [22; 23] utilized the fusion of infrared and visible images to improve nighttime visibility, while [PERSON] et al. [24] combined image inversion and exposure fusion to effectively address underexposure and overexposure issues. However, these methods often require input images from the same scene, making data acquisition costly, and are generally not specifically designed for glare suppression. In glare removal and adaptation to specific scenarios, researchers have integrated physical modeling with deep networks for improvement. [PERSON] et al. [25] proposed a unified variational retinal model for haze and glow suppression, while [PERSON] et al. [26] utilized HDR to expand the dynamic range for glare reduction. [PERSON] et al. [27] developed a layered decomposition and light effect suppression network for unsupervised light effect suppression and dark region enhancement, albeit with high computational complexity. [PERSON] et al. [28] designed a lightweight network for low-light glare removal and detail restoration, incorporating dynamic detection and modular processing for efficient resource allocation. [PERSON] et al. [29] further combined guided APSF simulation with gradient-adaptive convolution to achieve nighttime glare suppression, although domain discrepancies remain an issue. [PERSON] et al. [30] proposed OENet, an overexposure correction network that integrates Transformer structures to effectively fuse local and global information for detail enhancement; but, its large model size limits deployment on embedded devices. [PERSON] et al. [31] introduced GR-GAN, which utilizes a glare attention detector to adapt to irregular glare patterns for single-image glare removal, although it relies on paired supervised data for training. For glare removal in specific scenarios, [PERSON] et al. [32] proposed a nighttime reflection glare removal method based on optical central symmetry priors, leveraging the real-world glare dataset BracketFlare combined with a network-guided approach to enhance glare removal. [PERSON] et al. [33] proposed DTDN, which decomposes license plate images into occlusion, foreground, and roughly de-glared components to improve license plate recognition accuracy. [PERSON] et al. [34] combined physical modeling with semi-synthetic data, utilizing scattering and reflective glare models to generate realistic training pairs, and proposed a deep learning-based glare removal method that does not require real paired data, achieving effective glare removal on single RGB images.