## 2 Related Work
### PairLIE Method  
PairLIE, proposed by [PERSON] et al. [29], is an advanced LLIE method designed to enhance contrast and enrich image details in poor light conditions. Unlike traditional LLIE approaches that rely on a single input image and hand-crafted priors for illumination adjustments, PairLIE adopts a unique dual-input strategy. It utilizes two low-illuminated images with the same scene content as inputs, resulting in more adaptive and effective image enhancement. Figure 1 illustrates the PairLIE framework, which starts with the initial application of the P-Net module. This module removes noise and inappropriate features from the primary low-light images, denoted as \(l_{1}\) and \(l_{2}\), thereby refining the inputs for further processing. The L-Net and R-Net modules are then employed to extract the latent illumination components, \(L_{1}\) and \(L_{2}\), as well as the reflectance components, \(R_{1}\) and \(R_{2}\), for each pair of input images. During the training phase, a composite loss function is used to shape the network's performance, consisting of three distinct loss functions:  
\[L_{\text{Composite}}=w_{0}L_{P}+w_{1}L_{C}+w_{2}L_{R}, \tag{2}\]  
The influence of each constituent loss function, indicating their prescribed significance, is calibrated by the hyperparameters \(w_{0}\), \(w_{1}\), and \(w_{2}\). The self-supervised projection loss \(L_{P}\) assesses the variation between the unprocessed and refined images. Calculated using \(R_{1}\) and \(R_{2}\), the reflectance consistency loss \(L_{C}\) ensures consistency in the reflectance components. Additionally, the Retinex loss \(L_{R}\) enforces adherence to Retinex theory during the image decomposition process. During the testing stage, a given low-light image is initially preprocessed using the P-Net module. Then, the L-Net and R-Net modules are utilized to estimate the illumination and reflectance components, respectively. The final enhanced image is obtained by combining these components, resulting in improved brightness and detailed enhancement. This fusion process can be represented mathematically as:  
\[l_{\text{enhanced}}=g(L)\circ R=L^{\lambda}\circ R, \tag{3}\]  
Here, \(\lambda\) represents the illumination correction factor, and \(l_{\text{enhanced}}\) represents the resulting enhanced image.  
Figure 1: The architecture of PairLIE. The P-Net module is employed to eliminate unsuitable features present in the original image, while L-Net and R-Net are utilized to estimate the illumination and reflectance components of the image, respectively. The optimization process of the network is guided by three loss functions: A self-supervised projection loss \(L_{P}\), a reflectance consistency loss \(L_{C}\), and a Retinex loss \(L_{R}\). In the testing stage, PairLIE enhances the image by adjusting the illumination component and recombining it with the reflectance component, resulting in an enhanced output image.  
In contrast to the conventional methodologies that rely on manually designed priors, PairLIE integrates deep learning methods at the cutting edge with the well-established Retinex theory. This integration considerably diminishes the traditional approach's dependence on specific training datasets. Notably, PairLIE is proficient at maintaining the integrity of intricate textures and subtle details, which are frequently compromised under dim lighting conditions. This illustrates the method's advanced and refined processing prowess. Additionally, the architecture of PairLIE is characterized by a reduced reliance on hand-crafted priors and an architecture that is more straightforward, suggesting prospects for improved efficiency. However, its application in varied contexts may be constrained by its dependency on pairs of images driven by data, and its capacity to reproduce colors and brightness with accuracy may not always be adequate, underscoring opportunities for further enhancement in subsequent versions.