# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 1 Introduction  
With the continuous advancement of image processing and computer vision technologies, Low-Light Image Enhancement (LLIE) has emerged as a critical research area within computer vision. LLIE focuses on recovering visual information from images captured under insufficient illumination, addressing challenges such as underexposure, nonlinear sensor noise contamination, and structural detail loss. These issues are prevalent in various practical applications, including nighttime surveillance, autonomous driving, and medical imaging. For instance, in nighttime surveillance, low-light conditions often result in poor visibility, making it difficult to identify objects or individuals. Similarly, in autonomous driving, low-light environments can impair the performance of object detection and recognition systems, thereby posing significant safety risks. The ramifications extend to medical imaging diagnostics, where low-light conditions may lead to clinically consequential misinterpretations of pathological signatures due to poor image quality. Therefore, effectivelow-light image enhancement is essential for ensuring the successful completion of these high-level vision tasks and has become a subject worthy of further exploration. Conventional methods for low-light image enhancement, such as histogram equalization and Retinex-based techniques, have been widely used due to their simplicity and computational efficiency. However, these methods face several limitations. For example, Gray-level transformations struggle with higher luminance ranges, which can result in the loss of details in both over-exposed and under-exposed regions [1]. Histogram equalization variants such as Adaptive Histogram Equalization (AHE) and Contrast-Limited AHE (CLAHE) partially mitigate these issues but often introduce noise amplification and localized over-enhancement artifacts, ultimately degrading contrast homogeneity across spatial regions [2]. Retinex-inspired approaches, while theoretically grounded, suffer from two persistent drawbacks: (i) chromatic inaccuracies accompanied by halo artifacts near high-intensity light sources; (ii) non-generalizable parameter configurations requiring laborious per-image tuning--a critical limitation for real-world deployment [3]. In recent years, deep learning has offered a divergent path for improving low-light images by using complex neural network models. It has achieved remarkable success, with the groundbreaking Sustainable Illumination Decomposition (SID) method, which is particularly notable for processing extremely low-light images. Since its inception, SID has guided the evolution of deep learning architectures for LLIE. [PERSON] et al. [4] improved the SID methodology by simplifying the system architecture and introducing a novel amplifier module. This modification enables real-time LLIE restoration while directly utilizing dark, raw image intensity values for amplification ratio estimation. Architectural parallelism achieved an additional 30% acceleration, though without significant optimization in restoration quality. [PERSON] et al. [5] replaced the U-net architecture proposed by [PERSON] et al. [6] with residual learning to better preserve critical image features. Their method demonstrates superior performance in both Peak Signal-to-Noise Ratio (PSNR) and color representation compared to SID. Despite these advances, existing deep learning methods often overlook the importance of semantic information, leading to issues such as color distortion and loss of fine details. Semantic information serves as a pivotal element in low-light image enhancement, guiding networks to differentiate regional characteristics during enhancement and facilitating more vivid image restoration. However, existing approaches typically implement global and homogeneous improvements in low-light images without considering region-specific semantic contexts.