# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 4 Experiments and Results
### Quantitative Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Learning** & **Datasets** & **LOLv1** & \
\begin{tabular}{c} **LOLv2-** \\ **Real** \\ \end{tabular} &
\begin{tabular}{c} **LOLv2-** \\ **Synthetic** \\ \end{tabular} & **DICM** & **MEF** & **NPE** & **ExDark** & **Avg** \\ \hline \multirow{2}{*}{Conventional} & MSRCR & 6.239 & 6.253 & 13.077 & 12.443 & 12.056 & 15.938 & 10.016 & 10.860 \\  & Dong & 10.425 & 12.474 & 12.258 & 13.526 & 12.315 & 12.577 & 12.449 & 12.285 \\ \hline \multirow{2}{*}{Supervised} & KinD++ & 7.841 & 8.541 & 12.906 & 13.328 & 11.763 & 12.827 & 12.501 & 11.387 \\  & RetinexNet & 8.438 & 9.898 & 11.011 & 11.658 & 10.227 & 11.317 & 10.429 & 10.425 \\  & Retinexformer & 7.798 & 9.225 & 11.878 & 10.449 & 10.400 & 12.520 & 11.462 & 10.533 \\ \hline \multirow{6}{*}{Unsupervised} & EnlightGAN & 10.275 & 10.377 & 12.356 & 12.772 & 11.578 & 13.491 & 11.467 & 11.759 \\  & Zero-DCE++ & 13.049 & 13.602 & 13.667 & 14.640 & 13.801 & 13.870 & 13.370 & 13.714 \\ \cline{1-1}  & CLIP-LIT & 15.634 & 14.846 & 15.859 & 16.272 & 15.633 & 15.954 & 15.290 & 15.641 \\ \cline{1-1}  & NeRCo & 8.078 & 9.393 & 14.590 & 14.341 & 12.253 & 18.876 & 12.838 & 12.910 \\ \cline{1-1}  & Colie & 10.423 & 10.599 & 13.524 & 14.857 & 13.750 & 13.823 & 14.280 & 13.037 \\ \cline{1-1}  & Ours & 15.213 & 16.084 & 18.100 & 18.992 & 17.653 & 17.801 & 17.019 & 17.266 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison results of (LOLv1, LOLv2-real, LOLv2-synthetic, DICM, MEF, NPE, and ExDark) datasets using the PSNR metric. A higher value of the PSNR metric indicates a better performance. The first, second, and third best scores are highlighted with red, blue, and green colors, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Learning** & **Datasets** & **LOLv1** & \
\begin{tabular}{c} **LOLv2-** \\ **Real** \\ \end{tabular} &
\begin{tabular}{c} **LOLv2-** \\ **Synthetic** \\ \end{tabular} & **DICM** & **MEF** & **NPE** & **ExDark** & **Avg** \\ \hline \multirow{2}{*}{Conventional} & MSRCR & 0.153 & 0.119 & 0.455 & 0.466 & 0.361 & 0.527 & 0.342 & 0.346 \\  & Dong & 0.160 & 0.214 & 0.490 & 0.489 & 0.359 & 0.573 & 0.425 & 0.387 \\ \hline \multirow{2}{*}{Supervised} & KinD++ & 0.156 & 0.191 & 0.505 & 0.501 & 0.339 & 0.547 & 0.401 & 0.377 \\  & RetinexNet & 0.117 & 0.166 & 0.455 & 0.436 & 0.323 & 0.586 & 0.332 & 0.345 \\  & Retinexformer & 0.177 & 0.170 & 0.503 & 0.392 & 0.334 & 0.555 & 0.388 & 0.360 \\ \hline \multirow{2}{*}{Unsupervised} & EnlightGAN & 0.180 & 0.190 & 0.517 & 0.485 & 0.360 & 0.649 & 0.377 & 0.394 \\  & Zero-DCE++ & 0.264 & 0.275 & 0.545 & 0.528 & 0.429 & 0.634 & 0.442 & 0.445 \\ \cline{1-1}  & CLIP-LIT & 0.307 & 0.293 & 0.585 & 0.532 & 0.458 & 0.683 & 0.497 & 0.479 \\ \cline{1-1}  & NeRCo & 0.162 & 0.155 & 0.541 & 0.488 & 0.371 & 0.738 & 0.386 & 0.406 \\ \cline{1-1}  & Colie & 0.176 & 0.161 & 0.524 & 0.506 & 0.410 & 0.618 & 0.477 & 0.410 \\ \cline{1-1}  & Ours & 0.306 & 0.334 & 0.670 & 0.706 & 0.566 & 0.721 & 0.598 & 0.557 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison results of (LOLv1, LOLv2-real, LOLv2-synthetic, DICM, MEF, NPE, and ExDark) datasets using the SSIM metric. A higher value of the SSIM metric indicates better performance. The first, second, and third best scores are highlighted with red, blue, and green colors, respectively.