## 3 Methodology
### Quantitative Results  
The LOL-test dataset was initially utilized as the test data. Quantitative results in Table 4 illustrate a notable superiority of our method over other competitors, both in reference and no-reference metrics, thus demonstrating the effectiveness of our proposed combination approach with mixed weights. Specifically, our method achieves an improvement over the best performing method among the comparatives. The proposed method excels by achieving an improvement of 0.46 dB in terms of the PSNR metric, 0.0024 in terms of the SSIM metric, 0.0102 in terms of the LPIPS metric, and 0.2575 in terms of the NIQE metric. These quantitative results underscore the effectiveness of the proposed approach in enhancing the quality of low-light images. Subsequently, to ascertain the generalization capability of our proposed method, we conducted evaluations on the LOL-Real dataset. The corresponding results are displayed in Table 5. Our method once again exhibits superior performance compared to baseline methods across PSNR, SSIM, LPIPS, and NIQE metrics. Compared to other competing methods, while our method ranks second in terms of PSNR metric, it surpasses the comparative algorithms in other metrics to varying degrees. Overall, our approach demonstrates superior comprehensive performance. Analysis of data from Tables 4 and 5 reveals relatively poor results attained by traditional and unsupervised methods. This outcome is expected due to the inherent challenge of learning a robust enhancement model in the absence of a reference image. Furthermore, the efficacy of these methods heavily relies on hand-crafted priors. In summary, compared to the performance  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Coefficient Ratio** & **0.5:1** & **1:1** & **2:1** & **3:1** & **4:1** & **5:1** \\ \hline
**PSNR** & 21.32 & 21.63 & **21.70** & 21.48 & 21.55 & 21.53 \\ \hline \end{tabular}
\end{table}
Table 2: Corresponding average PSNR on the LOL-test dataset under different global weighted coefficient combination.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Layer Metric** & **PSNR** & **SSIM** & **NIQE** & **LPIPS** & **Score** \\ \hline
5 & 21.73 & 0.7840 & 3.8784 & 0.1323 & 0.60 \\
4 & 21.96 & 0.7869 & 3.8285 & 0.1341 & **0.70** \\
3 & 21.61 & 0.7615 & 3.7415 & 0.1541 & 0.40 \\
2 & 21.69 & 0.7936 & 3.9558 & 0.1272 & 0.65 \\
1 & 21.66 & 0.7940 & 3 of other methods on the aforementioned datasets, our approach maximizes the advantages of various LLIE methods, resulting in significant performance enhancements and superior overall performance.  
To showcase the robustness of our method, we conducted comparisons by randomly selecting 10 images from the DICM dataset, thereby imposing a more challenging scenario for image enhancement. The quantitative analysis comparing our method with competitors on the DICM dataset is summarized in Table 6. Specifically, our method demonstrates superior performance compared to several benchmarks. On average, it achieves a 0.4132 lower NIQE score than PairLIE, 0.0947 lower than Zero-DCE, 0.5834 lower than LIME, 1.9228 lower than R2R, 0.2962 lower than EnlightenGAN, 0.6483 lower than Ying, and 1.5721 lower than Night.  
In summary, the experimental results indicate that our method surpassed other state-of-the-art enhancers across three representative datasets. Notably, our approach achieves a significantly superior enhancing effect compared to any other stand-alone deep learning-based LLIE method, as it leverages both self-supervised and unsupervised approaches to their fullest extent. Functioning as a fusion-based enhancer, it integrates two complementary enhancers to amplify the enhancing effect, surpassing many representative state-of-the-art methods across various datasets.