# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 4 Experiments and Results
### Ablation Study
#### 4.4.2 Contribution of Each Loss  
To thoroughly evaluate the roles of each loss function in the proposed algorithm, we conduct tests on the LOLv1, LOLv2-real, and LOLv2-synthetic datasets. Qualitative and quantitative assessments are set up for scenarios where the proposed algorithm excludes  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**Methods**} & & & & \\
**DCE-L** & **DCE-C** & **MFF** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PIQE\(\downarrow\)** & **BRISQUE\(\downarrow\)** \\ \hline \(\surd\) & & & & 14.104 & 0.525 & 9.199 & 0.524 \\  & \(\surd\) & & 10.196 & 0.581 & 13.441 & 0.492 \\ \(\surd\) & \(\surd\) & & 13.198 & 0.544 & 9.540 & 0.532 \\  & \(\surd\) & \(\surd\) & 16.668 & 0.613 & 9.851 & 0.505 \\ \(\surd\) & & \(\surd\) & 17.656 & 0.611 & 8.177 & 0.538 \\ \(\surd\) & \(\surd\) & \(\surd\) & **18.149** & **0.664** & **8.116** & **0.474** \\ \hline \end{tabular}
\end{table}
Table 6: Quantitative comparison results of (DICM, MEF, and NPE) datasets using PSNR, SSIM, PIQE, and BRISQUE metrics. The best scores are highlighted in bold, where \(\uparrow\) means bigger values are better, and \(\downarrow\) means smaller values are better.  
Figure 16: The ablation study of the contribution of each module (DCE-L module, DCE-C module, MFF module).  
\(L_{spa}\) (without \(L_{spa}\), -w/o \(L_{spa}\)), \(L_{exp}\) (without \(L_{exp}\), -w/o \(L_{exp}\)), \(L_{col}\) (without \(L_{col}\), -w/o \(L_{col}\)), \(L_{tr_{A}}\) (without \(L_{tr_{A}}\), -w/o \(L_{tr_{A}}\)), and \(L_{ssim}\) (without \(L_{ssim}\), -w/o \(L_{ssim}\)).  
By comparing and analyzing the data presented in Figure 17 and Table 7, we can clearly observe that removing \(L_{spa}\) significantly reduces the contrast of the enhanced image, leading to severe image distortion. This change is directly reflected in the noticeable decline in PSNR and SSIM values, highlighting the indispensable role of \(L_{spa}\) in maintaining the difference in adjacent areas between the input and enhanced images. Similarly, excluding \(L_{exp}\) makes it difficult to effectively restore low-light areas, thereby adversely affecting the overall image quality, as evidenced by a significant increase in the PIQE value. Furthermore, when \(L_{col}\) is ignored, meaning the interrelationships among the three color channels are not adequately considered, this results in severe color distortion and an unnatural appearance of the image, leading to unsatisfactory PSNR and PIQE evaluations. Additionally, removing \(L_{tr_{A}}\) disrupts the correlation between adjacent areas of the image, causing obvious artifact phenomena and similarly resulting in severe image distortion, with both PSNR and SSIM values dropping substantially. Lastly, excluding \(L_{ssim}\) exacerbates image distortion, causing the significant loss of edge details and rendering the entire image unusually blurry, with both PSNR and SSIM values decreasing markedly. In contrast, when all loss functions are effectively integrated, the image enhancement effect reaches an optimal state. This not only demonstrates the unique contributions of each loss function in the image enhancement task but also emphasizes the importance of their mutual synergy and collective action in enhancing image quality.  
Figure 17: The ablation study of the contribution of each loss (\(L_{spa}\), \(L_{exp}\), \(L_{col}\), \(L_{tr_{A}}\), \(L_{ssim}\)).  
As shown in Table 7, after integrating all loss functions, the proposed method significantly achieves optimal values across all indicators. Therefore, both qualitative and quantitative evaluations indicate that all loss functions in the proposed method have a positive impact on the enhancement results.