## 3 Methodology  
### Experimental Observations  
[PERSON] et al. suggested in [31] that classical algorithms are constrained by the efficiencies of manual design and optimization-driven approaches. Their generalization and robustness are limited, restricting their applicability. To overcome these shortcomings, deep learning had been employed to construct complex mappings from low to normal lighting conditions [17; 32], significantly outperforming traditional LLIE methods [33; 34; 35]. However, [PERSON] et al. contend that most deep learning-based LLIE approaches focus on learning mapping functions, but ignore the guidance of auxiliary priors provided by normal-light images in the training dataset, resulting in enhanced images exhibiting unpleasant artifacts or distorted colors [11]. As illustrated in Figure 3, none of the LLIEs showcase a definitive advantage in terms of detail preservation, exposure, color fidelity, and other aspects. Both traditional techniques such as LIME, Ying [36], and Retinex-Net, as well as deep learning methods like EnlightenGAN and PairLIE, primarily focus on preserving fine details. However, this focus frequently results in the unintended consequence of introducing noise. In terms of contrast, Zero-DCE and PairLIE serve as exemplars of the  
Figure 2: The architecture of Night. Upon receiving an input night image, _Night_ employs a layer decomposition network for the suppression of undesired light effects. This intricate network is adept at the extraction of light effects, shading, and reflectance layers, thereby facilitating their individual analysis and manipulation. Central to the architecture is an unsupervised light-effects suppression network, guided by the decomposed light-effects layer \(G\). This mechanism capitalizes on unpaired learning techniques to diminish the prominence of light effects, while concurrently enhancing the visibility within the darker regions of the image. A distinctive feature of this unsupervised approach is its generation of an attention map. This map vividly delineates the network’s targeted focus on areas predominantly influenced by light effects, ensuring a precise and effective suppression strategy. Consequently, this approach leads to a notable reduction of light effects in the refined output \(J_{\text{refine}}\).  
deep learning paradigm. When evaluating color fidelity, deep learning-based methods such as LLFlow [37], Night, and SCI [38] excel. LLFlow employs supervised learning, while Night and SCI rely on unsupervised approaches. Notably, LLFlow and Night demonstrate superior color reproduction, though they may face challenges in maintaining appropriate brightness levels. Night, specifically, stands out in mitigating overexposure. In terms of exposure correction, Bread [39] and LLFlow emerge as prominent contenders, achieving a more balanced distribution of light and dark regions in images. Furthermore, Night excels in reducing artifacts, effectively minimizing issues like halos and unnatural edges.  
Therefore, we can naturally draw the following conclusion: no single LLIE method consistently outperforms others across all conditions. Instead, each method offers distinct and complementary advantages. This observation underscores the potential for developing hybrid approaches that combine the strengths of individual methods synergistically, thereby providing a more robust solution for enhancing low-light images.