# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 4 Experiments and Results
### Ablation Study
#### 4.4.3 Contribution of Each Loss Weight  
To validate the effectiveness of the proposed algorithm, we conduct tests on the DICM, MEF, and ExDark datasets. Drawing inspiration from the parameter settings of the Zero-DCE algorithm, we initially set the weight parameters \(W_{total}\), \(W_{col}\), and \(W_{tv_{A}}\) to 1, 0.5, and 20, respectively, using these values as the starting points for our experiments.  
We adopt a systematic approach to parameter tuning. Firstly, with \(W_{col}\) and \(W_{tv_{A}}\) fixed, we incrementally adjust the value of \(W_{total}\) from 0 to 1 to observe its impact on image quality. The experimental results indicate that as \(W_{total}\) increases, image brightness gradually decreases, and overexposure is mitigated. However, when \(W_{total}\) becomes excessively large, overexposure gradually reappears. By the time \(W_{total}\) reaches 1, which means the influence of \(L_{ssim}\) is eliminated, although the overall effect improves, color distortion issues arise. Subsequently, we keep \(W_{total}\) and \(W_{tv_{A}}\) constant while incrementally adjusting the value of \(W_{col}\) from 0 to 1. Experiments reveal that as \(W_{col}\) increases, image brightness first decreases and then increases. When \(W_{col}\) is too large, visual differences become less apparent, but overexposure becomes more significant. Finally, we fix the values of \(W_{total}\) and \(W_{col}\) while incrementally adjusting \(W_{tv_{A}}\) from 0 to 30. Experimental results demonstrate that when \(W_{tv_{A}}\) is either too large or too small, this leads to the loss of image edge details and a decrease in contrast, rendering the overall image blurred. Figures 18-20 intuitively demonstrate the impact of these parameters on algorithm performance.  
Additionally, Figure 21 presents the average values of PSNR, SSIM, PIQE, and BRISQUE for the three datasets under different parameter settings. These graphs further validate our experimental results, confirming that the algorithm achieves the best enhancement effect when \(W_{total}\), \(W_{col}\), and \(W_{tv_{A}}\) are set to 0.5, 0.5, and 20, respectively.  
In summary, through systematic parameter tuning and experimental validation, we determine the optimal combination of parameters. This finding is not only supported by subjective visual assessments but is also further corroborated by objective evaluation indicators.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(L_{spa}\)} & \multicolumn{3}{c}{**Methods**} & \multirow{2}{*}{\(L_{exp}\)} & \multirow{2}{*}{\(L_{col}\)} & \multirow{2}{*}{\(L_{tr_{A}}\)} & \multirow{2}{*}{\(L_{ssim}\)} & \multirow{2}{*}{\(\mathbf{PSNR}\uparrow\)} & \multirow{2}{*}{\(\mathbf{SSIM}\uparrow\)} & \multirow{2}{*}{\(\mathbf{BRISQUE}\downarrow\)} & \multirow{2}{*}{\(\mathbf{PIQE}\downarrow\)} \\ \cline{1-1}  & & & & & & & & \\ \hline \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 7.428 & 0.184 & 0.506 & 16.527 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 13.434 & 0.123 & 0.497 & 24.781 \\ \(\surd\) & \(\surd\) & & \(\surd\) & \(\surd\) & 12.475 & 0.345 & 0.504 & 16.924 \\ \(\surd\) & \(\surd\) & \(\surd\) & & \(\surd\) & 8.048 & 0.124 & 0.495 & 16.602 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & 7.674 & 0.223 & 0.496 & 18.886 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & **16.466** & **0.437** & **0.492** & **13.890** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quantitative comparison results of (LOLv1, LOLv2-real, and LOLv2-synthetic) datasets using PSNR, SSIM, BRISQUE, and PIQE metrics. The best scores are highlighted in bold, where \(\uparrow\) means bigger values are better, and \(\downarrow\) means smaller values are better.  
Figure 19: The ablation study of the contribution of \(W_{col}\).  
Figure 20: The ablation study of the contribution of \(W_{l_{D},A}\).