# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
## 4 Results
### Quantitative Comparisons
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**LPIPS \(\downarrow\)**} \\ \cline{2-9}
**Method** & \multicolumn{2}{c}{**LOLv1**} & \multicolumn{2}{c}{**LOLv2-Real**} & \multicolumn{2}{c}{**LOLv2-**} & \multicolumn{2}{c}{**LSRW-**} \\  & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Std.**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Std.**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Std.**} \\ \hline RetinexNet & 0.364 & 0.098 & 0.425 & 0.110 & 0.199 & 0.062 & 0.360 & 0.072 \\ Zero-DCE & 0.255 & **0.075** & 0.268 & 0.097 & **0.140** & **0.048** & 0.293 & 0.072 \\ R2 RNet & **0.170** & **0.058** & **0.183** & **0.063** & 0.226 & 0.078 & **0.260** & **0.059** \\ RUAS & **0.180** & 0.088 & **0.206** & **0.062** & 0.281 & 0.091 & 0.346 & 0.102 \\ Zero-DCE++ & 0.233 & **0.078** & 0.228 & **0.091** & **0.129** & **0.053** & **0.260** & **0.064** \\ CLIP-LIT & 0.274 & 0.101 & 0.256 & 0.117 & 0.164 & 0.073 & 0.285 & **0.070** \\ GCP & 0.331 & 0.115 & 0.349 & 0.143 & 0.153 & 0.077 & 0.308 & 0.102 \\ CoLIE & 0.260 & 0.107 & 0.259 & 0.124 & **0.140** & 0.066 & 0.286 & 0.087 \\ Ours & **0.215** & 0.084 & **0.217** & 0.100 & **0.111** & **0.052** & **0.253** & 0.072 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of reference metric (LPIPS) across LOLv1, LOLv2-Real, LOLv2-Synthetic, and LSRW-huawei datasets. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**PSNR \(\uparrow\)**} & \multicolumn{6}{c}{**SSIM \(\uparrow\)**} \\ \cline{2-9}  & **LOLv1** & **LOLv2-Real** & **LOLv2-Synthetic** & **LSRW-** & **LOLv1** & **LOLv2-Real** & **LOLv2-Synthetic** & **LSRW-** \\ \hline RetinexNet & 16.774 & 16.097 & 17.137 & 16.819 & 0.546 & 0.440 & **0.795** & 0.556 \\ Zero-DCE & 16.752 & 17.866 & **18.070** & **17.956** & 0.461 & 0.494 & 0.538 & 0.451 \\ R2 RNet & **18.179** & **17.949** & 16.089 & **17.277** & **0.605** & **0.654** & 0.524 & 0.554 \\ RUAS & 16.405 & 15.326 & 13.404 & 15.690 & 0.553 & 0.485 & 0.549 & **0.571** \\ Zero-DCE++ & 15.901 & **18.352** & **18.223** & 17.138 & 0.433 & 0.507 & 0.536 & 0.418 \\ CLIP-LIT & 12.394 & 15.182 & 16.190 & 13.561 & 0.526 & **0.563** & **0.718** & 0.555 \\ GCP & **17.393** & 16.610 & 16.620 & 16.550 & 0.521 & 0.436 & 0.668 & 0.541 \\ CoLIE & 16.310 & 16.910 & 17.697 & 17.249 & **0.565** & 0.516 & **0.751** & **0.590** \\ Ours & **18.065** & **19.044** & **19.309** & **18.940** & **0.610** & **0.606** & 0.673 & **0.619** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of reference metrics (PSNR and SSIM) across LOLv1, LOLv2-Real, LOLv2-Synthetic, Synthetic, and LSRW-huawei datasets. the best metric on the LOLv2-Synthetic dataset, outperforming all the other methods. Additionally, on the LOLv1, LOLv2-Real, and LSRW-huawei datasets, our NIQE metrics consistently rank within the top three, further validating the effectiveness of our approach. It should be noted that, although the proposed method does not rank in the top three on the DICM and MEF datasets, the difference in performance metrics compared to the third-ranked algorithm is actually quite small and very close.