## 3 Methodology
### Datasets and Experimental Setup  
To comprehensively assess the overall enhancement performance of the proposed enhancement model, extensive experiments were conducted. The evaluation was carried out using three benchmark datasets: the LOL dataset [19], consisting of 485 pairs of low/normal light images for training and 15 for evaluation; LOL-Real [42], comprising 100 testing images with more diverse scenes; and DICM [43], introduced by [PERSON] et al., containing 69 images captured using a commercial digital camera. These images are particularly valuable for studying image enhancement under low-light conditions. Specifically, in the ablation experiment, only the LOL-test dataset was utilized. Evaluation was performed on the LOL-test dataset, LOL-Real test-split, and DICM (with 10 images randomly selected). Representative state-of-the-art methods used for comparison can be categorized into four groups: traditional methods (e.g., LIME, Ying), supervised approaches (e.g., R2R), self-supervised methods (e.g., PairLIE), and unsupervised methods (e.g., Zero-DCE, Enlighten, Night), all demonstrating significant enhancement results. Objective evaluation of enhancing methods employed image quality assessment metrics, namely peak signal-to-noise ratio (PSNR), SSIM [44], NIQE, and learned perceptual image patch similarity (LPIPS) [45]. PSNR measures intensity similarity between ground truth and generated images, with higher values indicating better enhancement capability. SSIM, aligned with human visual perception, indicates visually more satisfying results with higher values. LPIPS, a reference-based metric, quantifies perceptual similarity using deep learning features, with lower values indicating higher perceptual resemblance and superior image quality. NIQE, a no-reference metric, assesses image naturalness through statistical modeling, with lower scores implying a more natural appearance and enhanced image quality. Experiments were conducted using PyTorch on a single NVIDIA RTX 3090 GPU and a Lenovo desktop equipped with a 2.1 GHz Intel Core i7-6700k CPU and 16 GB of RAM. Additionally, to comprehensively evaluate various LLIEs based on four evaluation metrics (PSNR, SSIM, NIQE, and LPIPS), we have implemented the following scoring mechanism. For metrics such as PSNR and SSIM, where higher values indicate better enhancement, we ranked the metric values obtained by the compared methods in ascending order. The method with the highest PSNR value among all evaluated methods is assigned a rank of \(n\), where \(n\) represents the total number of methods evaluated. The method ranked first receives a score of \(n\) points, the second-ranked method receives \(n-1\) points, and so on. Conversely, for metrics like LPIPS and NIQE, where lower values indicate improved quality, we sorted the metric values in descending order. Using a similar scoring mechanism as for PSNR and SSIM, we assigned scores based on the rankings. Next, we calculated the total score for each method by multiplying its scores on each metric by the corresponding metric's weight. The total score is obtained by summing these weighted scores. Finally, we normalized the scores to a range of 0 to 1 by dividing the original score by the maximum possible score. The maximum score is determined by the sum of weights of all metrics multiplied by the total number of methods evaluated. \[ \begin{split}&\text{Score}=(W_{\text{PSnr}}\cdot N_{\text{PSnr}}+W_{ \text{SSim}}\cdot N_{\text{SSim}}+W_{\text{NIQE}}\cdot N_{\text{NIQE}}\\ +W_{\text{LPIPS}}\cdot N_{\text{LPIPS}})/\{m\cdot(W_{\text{PSnr} }+W_{\text{SSim}}+W_{\text{NIQE}}+W_{\text{LPIPS}})\},\end{split}  \tag{9}\]  
In the scoring formula, \(m\) represents the total number of metrics used in the evaluation. The values \(N_{\text{PSnr}}\), \(N_{\text{SSim}}\), \(N_{\text{NIQE}}\), and \(N_{\text{LPIPS}}\) correspond to the rankings of each method based on their respective PSNR, SSIM, NIQE, and LPIPS metrics.