# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Quantitative Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Satellite Name** & **Metrics** & **SSR 1** & **Gamma (\(\gamma\) = 0.6/0.8) 2** &
\begin{tabular}{c} **Gamma + CLAHE** \\ **(\(\gamma\) = 0.6/0.8)** \\ \end{tabular} & **NeRCo** & **Retineformer** & **Ours** \\ \hline \multirow{3}{*}{AcrimSat} & PSNR \(\uparrow\) & 28.98 & 27.52 (31.17 3) & 27.59 (27.58) & 27.80 & 27.46 & 36.67 3 \\  & SSIM \(\uparrow\) & 0.1041 & 0.5102 (0.8321) & 0.3272 (0.5176) & 0.2012 & 0.2097 & 0.8724 \\  & LPIPS \(\downarrow\) & 0.821 & 0.288 (0.096) & 0.488 (0.289) & 0.804 & 0.747 & 0.117 \\ \hline \multirow{3}{*}{Aquarius} & PSNR \(\uparrow\) & 28.92 & 27.60 (31.38) & 27.59 (27.79) & 27.98 & 27.54 & 37.87 \\  & SSIM \(\uparrow\) & 0.1078 & 0.5046 (0.8330) & 0.2904 (0.4844) & 0.2066 & 0.2225 & 0.9003 \\  & LPIPS \(\downarrow\) & 0.841 & 0.314 (0.104) & 0.535 (0.307) & 0.819 & 0.761 & 0.097 \\ \hline \multirow{3}{*}{Aura} & PSNR \(\uparrow\) & 28.75 & 27.53 (31.89) & 27.52 (27.71) & 27.76 & 27.54 & 35.65 \\  & SSIM \(\uparrow\) & 0.0805 & 0.4871 (0.8292) & 0.2580 (0.4482) & 0.1132 & 0.2006 & 0.7946 \\  & LPIPS \(\downarrow\) & 0.834 & 0.317 (0.111) & 0.532 (0.315) & 0.829 & 0.748 & 0.245 \\ \hline \multirow{3}{*}{Calipso} & PSNR \(\uparrow\) & 28.88 & 27.60 (02.33) & 27.57 (27.27) & 27.97 & 27.67 & 36.95 \\  & SSIM \(\uparrow\) & 0.0705 & 0.4635 (0.8177) & 0.2600 (0.4438) & 0.1672 & 0.2050 & 0.8107 \\  & LPIPS \(\downarrow\) & 0.869 & 0.339 (0.119) & 0.541 (0.321) & 0.837 & 0.770 & 0.233 \\ \hline \multirow{3}{*}{CloudSat} & PSNR \(\uparrow\) & 31.62 & 31.44 (35.13) & 30.08 (30.58) & 27.77 & 31.87 & 38.05 \\  & SSIM \(\uparrow\) & 0.2077 & 0.5870 (0.8970) & 0.2873 (0.3783) & 0.1086 & 0.5463 & 0.9021 \\  & LPIPS \(\downarrow\) & 0.665 & 0.241 (0.073) & 0.327 (0.217) & 0.676 & 0.357 & 0.040 \\ \hline \multirow{3}{*}{CubeSat} & PSNR \(\uparrow\) & 28.82 & 27.59 (31.81) & 27.61 (27.66) & 27.89 & 27.54 & 35.61 \\  & SSIM \(\uparrow\) & 0.0734 & 0.4810 (0.8214) & 0.2835 (0.4611) & 0.1899 & 0.2235 & 0.7820 \\  & LPIPS \(\downarrow\) & 0.860 & 0.324 (0.112) & 0.529 (0.318) & 0.836 & 0.758 & 0.2233 \\ \hline \multirow{3}{*}{Jason} & PSNR \(\uparrow\) & 28.43 & 27.61 (32.68) & 27.50 (27.60) & 27.91 & 27.82 & 37.34 \\  & SSIM \(\uparrow\) & 0.0724 & 0.4495 (0.8154) & 0.2494 (0.4123) & 0.1415 & 0.2081 & 0.8863 \\  & LPIPS \(\downarrow\) & 0.850 & 0.334 (0.125) & 0.513 (0.321) & 0.809 & 0.691 & 0.126 \\ \hline \multirow{3}{*}{Sentinel-6} & PSNR \(\uparrow\) & 28.76 & 27.58 (32.52) & 27.55 (27.69) & 27.84 & 27.85 & 36.87 \\  & SSIM \(\uparrow\) & 0.0710 & 0.4559 (0.8142) & 0.2556 (0.4323) & 0.1578 & 0.2155 & 0.8235 \\  & LPIPS \(\downarrow\) & 0.886 & 0.350 (0.124) & 0.549 (0.336) & 0.863 & 0.757 & 0.229 \\ \hline \multirow{3}{*}{Terra} & PSNR \(\uparrow\) & 28.92 & 27.52 (31.79) & 27.53 (27.70) & 27.87 & 27.57 & 36.70 \\  & SSIM \(\uparrow\) & 0.0917 & 0.4860 (0.8276) & 0.2676 (0.4504) & 0.1827 & 0.2063 & 0.8866 \\  & LPIPS \(\downarrow\) & 0.854 & 0.322 (0.115) & 0.537 (0.323) & 0.845 & 0.754 & 0.100 \\ \hline \multirow{3}{*}{TRMM} & PSNR \(\uparrow\) & 28.92 & 27.49 (32.17) & 27.55 (27.70) & 27.91 & 27.57 & 37.42 \\  & SSIM \(\uparrow\) & 0.0862 & 0.4675 (0.8186) & 0.2637 (0.4454) & 0.1667 & 0.1998 & 0.8727 \\ \cline{1-1}  & LPIPS \(\downarrow\) & 0.877 & 0.338 (0.121) & 0.542 (0.325) & 0.852 & 0.778 & 0.160 \\ \hline \hline \end{tabular}  
* \({}^{1}\) SSR denotes Single-scale Retinex. 2 Gamma (\(\gamma\) = 0.6/0.8)) represents \(\gamma\) = 0.6 and \(\gamma\) = 0.8, respectively. 3 In the table, values highlighted in red indicate the best performance, whereas values in blue denote the second-best performance.  
\end{table} 
Table 3: Quantitative evaluation of multiple methods on satellite images containing Earth region. The experimental results show that the proposed method outperforms traditional methods and mainstream deep learning models in qualitative and quantitative evaluation.