# 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  
Recent years have witnessed remarkable progress in neural networks [6; 15; 16] for this domain. The learning strategies employed encompass supervised learning (SL), reinforcement learning (RL), unsupervised learning (UL), zero-shot learning (ZSL), and semi-supervised learning (SSL). Supervised learning trains models through paired datasets where input-output mappings are explicitly defined. For LLIE, this involves using low/normal-light image pairs for training. The pioneering deep learning work in LLIE was LLNet [15], which employed stacked sparse denoising autoencoders for simultaneous low-light brightening and denoising. MBLLEN [16] first extended LLIE techniques to low-light video restoration. Subsequently, [PERSON] et al. proposed \"Learning to See in the Dark\" (SID) [6], introducing an end-to-end network alongside the SID dataset containing raw short-exposure low-light images and corresponding long-exposure references. Despite being a milestone for extreme low-light restoration, SID exhibits two critical limitations: (1) extremely high computational costs hinder deployment on resource-constrained devices; (2) reliance on prior gain knowledge about real exposure levels, which proves impractical in real-world scenarios. Addressing these issues, [PERSON] et al. [4] proposed a lightweight parallel architecture with a novel amplifier module that estimates magnification factors directly from input intensities, achieving 30% acceleration without quality degradation. Recent advancements integrate Retinex theory with data-driven enhancement. KinD [17] and KinD++ [18] combined Retinex decomposition with deep networks for illumination adjustment. [PERSON] et al. [19] developed the EEMEFN framework with multi-exposure fusion and edge enhancement stages. Other notable architectures include luminance pyramid networks (LPNet) [20], residual networks [21], and Laplacian pyramid-based DSLR [22]. Recently, [PERSON] et al. proposed FourLLIE [23], incorporating Fourier frequency information through amplitude transformation estimation in Fourier space and SNR-guided spatial detail restoration. (2) Unsupervised Learning  
In Low-Light Image Enhancement (LLIE), unsupervised learning refers to a learning strategy without paired images. Unsupervised learning methods learn from unlabeled data, providing a novel approach to tackle the problem of low-light image enhancement. For instance, unsupervised learning might utilize one dataset of low-exposure images and another of normally exposed images. Compared to supervised learning, unsupervised learning does not require a vast amount of labeled data, making it more suitable for practical scenarios where high-quality labels are scarce. However, unsupervised learning also faces some challenges in low-light image enhancement, such as difficulties in evaluation due to the lack of label information and network generalization performance. To address these challenges, researchers have proposed a range of innovative solutions, including adaptive loss function design and adversarial training strategies. Among them, EnlightenGAN [24] regularizes unpaired training by extracting information from the input itself and introduces a series of innovations for low-light image enhancement, including a global-local discriminator structure, self-regularized perceptual loss fusion, and attention mechanisms. It employs an attention-guided U-Net as the generator to ensure that the enhanced results resemble real, normally illuminated images. Additionally, they proposed global and local self-feature preservation losses to preserve the image content before and after enhancement. This is crucial for stabilizing the training of such unidirectional Generative Adversarial Network (GAN) structures. (3) Semi-Supervised Learning  
Semi-supervised learning is a learning approach that lies between supervised learning and unsupervised learning.