## 3 Methodology
### Qualitative Comparison  
In the task of image enhancement, visual quality serves as a crucial metric for evaluating the performance of LLIE methods. To visually compare the enhancing effect of our proposed method, we employed various competing methods to enhance images in the LOL-test dataset. Subsequently, we calculated the PSNR values of the enhanced images. For better visualization, we focused specifically on enlarging the area enclosed by the blue box to enhance clarity. As depicted in Figure 6, in terms of contrast, EnlightenGAN, LIME, and Night face challenges in achieving balanced contrast. Specifically, EnlightenGAN and Night exhibit low contrast, while LIME shows excessive contrast. Regarding overall brightness, PairLIE and Ying tend to produce darker images overall. Notably, Zero-DCE and \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Metric Method & PairLIE & Zero-DCE & LIME & R2R & EnlightenGAN & Ying & Night & Our \\ \hline
**NIQE** & 2.9694 & 2.6509 & 3.1396 & 4.4790 & 2.8524 & 3.2045 & 4.1283 & **2.5562** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantitative evaluation of different LLIEs on DICM datasets (10 pictures selected). The best results are results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Metric Method & PairLIE & Zero-DCE & LIME & R2R & Night & EnlightenGAN & Ying & Our \\ \hline PSNR & 18.46 & 16.80 & 17.19 & 18.12 & 21.50 & 16.12 & 14.46 & **21.96** \\ SSIM & 0.7431 & 0.5644 & 0.5628 & 0.7381 & 0.7625 & 0.6623 & 0.5277 & **0.7869** \\ LPIPS & 0.1621 & 0.2486 & 0.1443 & 0.1864 & 0.1757 & 0.2058 & 0.2470 & **0.1341** \\ NIQE & 4.1014 & 7.9335 & 5.5859 & 4.0860 & 4.4938 & 6.2705 & 7.7122 & **3.8285** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative evaluation of different LLIEs on LOL-test datasets. The best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline Metric Method & PairLIE & Zero-DCE & LIME & R2R & Night & EnlightenGAN & Ying & Our \\ \hline PSNR & 19.93 & 18.19 & 18.10 & 17.95 & **25.53** & 19.39 & 17.32 & 23.22 \\ SSIM & 0.7731 & 0.5542 & 0.5493 & 0.7733 & 0.7822 & 0.7035 & 0.5955 & **0.8203** \\ LPIPS & 4.3791 & 8.2194 & 5.2983 & 4.9589 & 4.3157 & 6.3506 & 7.9347 & **3.8818** \\ NIQE & 0.1582 & 0.2540 & 0.1649 & 0.1760 & 0.1883 & 0.1781 & 0.2026 & **0.1340** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative evaluation of different LLIEs on LOL-Real datasets. The best results are highlighted in bold. R2R induce color distortions. In terms of details and noise, traditional methods typically introduce varying levels of noise. Conversely, deep learning-based LLIEs often overly smooth the images to eliminate noise, as observed in Night, or retain details but struggle to effectively reduce noise, as exemplified by PairLIE. Regarding the pre-enhanced images used as target images during the image fusion stage, Night excels in maintaining overall brightness and color accuracy. However, it tends to fall short in preserving fine details. In contrast, PairLIE excels in detail preservation but is more conservative in enhancing brightness. Our proposed method uniquely strikes a balance among these aspects, successfully preserving details, minimizing noise, and maintaining color fidelity. It ultimately achieves a brightness level and natural color contrast that closely resemble those of the original images. Furthermore, Figure 7 presents the results of all methods on the LOL-Real dataset. In the magnification box, it can be observed that while traditional methods perform well in preserving details, they often introduce some level of noise. On the other hand, deep learning-based LLIE methods excel at preserving details but face challenges of excessive smoothing. Additionally, in terms of overall color fidelity and natural color contrast, Zero-DCE and LIME exhibit excessive contrast, Night demonstrates low contrast, and R2R suffers from color distortion.