# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 2 Related Work
### Low-Light Image Enhancement
#### 2.1.2 Deep Learning-Based Methods
In Low-Light Image Enhancement (LLIE), semi-supervised learning can be understood as a learning strategy with a small number of paired images and many unpaired images. A representative method of semi-supervised learning in LLIE is DRBN [25]. It proposes a deep recursive band network for semi-supervised low-light enhancement. By introducing Long Short-Term Memory (LSTM) networks and an image quality assessment network pre-trained on an aesthetic visual analysis dataset, better enhancement performance is achieved. The advantage of semi-supervised learning lies in its ability to more fully utilize existing data, especially when it is costly or difficult to obtain a large amount of labeled data. For LLIE, semi-supervised learning can help the model better adapt to different scenes and lighting conditions, improving its generalization ability. (4) Zero-Shot Learning  
Supervised and unsupervised learning methods either exhibit limited generalization capabilities or suffer from unstable training processes. Supervised learning demonstrates strong dependence on the training dataset--when the parameter differences between low-light images and the training set become significant, the quality of restored images deteriorates dramatically. Moreover, collecting sufficiently effective training data itself constitutes a challenging task. To enhance generalization capacity, zero-shot learning proposes to learn enhancement solely from test images. The learned model becomes image-specific, and this approach inherently adapts to different settings of unseen images. Notably, in low-level vision tasks, the concept of zero-shot learning emphasizes that the method requires neither paired nor unpaired training data, which differs from its definition in high-level vision tasks. In LLIE, [PERSON] et al. [26] first introduced zero-shot learning for low-light image restoration. They proposed the ExCNet framework where, given a test backlit image I, ExCNet estimates the optimal \"s-curve\" parameters for I within limited iterations. A block-based loss function was designed to maximize visibility across all image blocks while preserving relative differences between adjacent blocks, ensuring properly exposed outputs. [PERSON] et al. [27] developed the RRDNet with three branches that explicitly predict illumination, reflectance, and noise components of input images. This architecture relies on internal optimization for individual inputs, guaranteeing generalization across diverse shooting scenarios and illumination conditions. Furthermore, they proposed a novel loss function to preserve rich textural details in restored results through decomposition optimization. [PERSON] et al. [28] presented the RUAS network. Unlike CNN-based LLIE methods requiring heuristic neural architecture design, RUAS first establishes a principled approach for constructing base network structures, then automatically discovers embedded atomic prior architectures. A cooperative two-level search strategy was developed for RUAS to simultaneously discover architectures from a compact search space for illumination estimation and noise removal. Empowered by zero-reference loss functions, zero-shot learning methods demonstrate remarkable generalization capabilities while requiring minimal parameters and achieving fast inference speeds. Unsupervised learning, semi-supervised learning, and zero-shot learning, despite their respective merits, present inherent constraints: (1) The absence of ground-truth labels in unsupervised learning may result in unstable model performance, particularly in complex scenes. The lack of supervisory signals complicates optimization processes, potentially causing models to converge to suboptimal solutions. (2) Semi-supervised methods inherit limitations from both supervised and unsupervised paradigms while failing to fully leverage their respective strengths, resulting in limited practical adoption within real-world applications.