# Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition
## 3 Results and Discussions
### Comparisons of Image Enhancement Methods
The comparative analysis of experimental data from outdoor settings is depicted in Figure 6. This figure illustrates six distinct outdoor scenarios, each represented in columns (a) to (f). Sequentially from top to bottom, each set presents the original images followed by the results processed using CLAHE, SSR, AMSR, and the algorithm proposed herein. Analogous to indoor findings, the restoration quality of CLAHE sees improvement due to the diminished irregularities in lighting. Despite SSR's capacity to restore images, it continues to yield comparatively low contrast. The AMSR here demonstrates even lower contrast than the SSR. HMHE successfully recovers critical visual elements in outdoor scenes. Quantitative assessments are shown in Table 2, where the best scores are highlighted with bold text. It can be seen in outdoor conditions that HMHE has an advantageous qualitative score. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Figure** & **IQA** & **CLAHE** & **SSR** & **AMSR** & **Ours** \\ \hline \multirow{4}{*}{Figure 5d} & IE & **7.95973** & 4.86909 & 6.70503 & 7.68406 \\  & SSIM & 0.02726 & **0.41822** & 0.12946 & 0.23182 \\  & FSIM & 0.39348 & **0.80907** & 0.67776 & 0.57946 \\  & CORR & 0.31503 & 0.35087 & 0.36047 & **0.72899** \\ \hline \multirow{4}{*}{Figure 5e} & IE & **7.95370** & 4.98626 & 6.73997 & 7.60437 \\  & SSIM & 0.02071 & **0.43820** & 0.11987 & 0.33159 \\  & FSIM & 0.41044 & **0.78605** & 0.66660 & 0.68101 \\  & CORR & 0.28866 & 0.40698 & 0.42350 & **0.66119** \\ \hline \multirow{4}{*}{Figure 5f} & IE & **7.91214** & 6.54692 & 6.69851 & 7.56860 \\  & SSIM & 0.03088 & 0.11613 & 0.09479 & **0.21201** \\ \cline{1-1}  & FSIM & 0.37762 & 0.48803 & 0.47302 & **0.50114** \\ \cline{1-1}  & CORR & 0.09291 & 0.10340 & **0.10464** & 0.05351 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Figure** & **IQA** & **CLAHE** & **SSR** & **AMSR** & **Ours** \\ \hline \multirow{4}{*}{Figure 6a} & IE & **7.92424** & 6.76166 & 6.64734 & 7.50889 \\  & SSIM & 0.13068 & 0.41596 & 0.30377 & **0.62899** \\  & FSIM & 0.33459 & 0.65163 & 0.66496 & **0.68451** \\  & CORR & 0.50456 & 0.53852 & 0.54223 & **0.94058** \\ \hline \multirow{4}{*}{Figure 6b} & IE & 7.87551 & 6.79691 & 6.73705 & **7.88411** \\  & SSIM & 0.06829 & 0.19673 & 0.15419 & **0.25255** \\  & FSIM & 0.26334 & 0.49349 & 0.47663 & **0.51187** \\  & CORR & 0.30587 & 0.30300 & 0.31409 & **0.94419** \\ \hline \multirow{4}{*}{Figure 6c} & IE & 7.62235 & 6.78204 & 6.72882 & **7.80304** \\  & SSIM & 0.10018 & 0.19934 & 0.16949 & **0.38536** \\  & FSIM & 0.47190 & 0.64911 & 0.64058 & **0.68164** \\  & CORR & 0.27570 & 0.17789 & 0.18539 & **0.95386** \\ \hline \multirow{4}{*}{Figure 6d} & IE & **7.82762** & 6.58170 & 6.72037 & 7.57464 \\  & SSIM & 0.09130 & 0.24309 & 0.12753 & **0.53592** \\  & FSIM & 0.41575 & 0.67563 & 0.62096 & **0.72060** \\  & CORR & 0.45556 & 0.35883 & 0.36952 & **0.92809** \\ \hline \multirow{4}{*}{Figure 6e} & IE & 7.07433 & 6.41182 & 6.49151 & **7.70322** \\  & SSIM & 0.36827 & 0.41095 & 0.28306 & **0.54742** \\ \cline{1-1}  & FSIM & 0.57004 & 0.66446 & 0.63293 & **0.68455** \\ \cline{1-1}  & CORR & 0.24727 & 0.22330 & 0.24671 & **0.95100** \\ \hline \multirow{4}{*}{Figure 6f} & IE & **7.93681** & 5.46808 & 6.58479 & 7.86451 \\  & SSIM & 0.05784 & 0.24139 & 0.11950 & **0.52467** \\ \cline{1-1}  & FSIM & 0.23767 & 0.76811 & 0.50903 & **0.79610** \\ \cline{1-1}  & CORR & 0.57649 & 0.62285 & 0.61980 & **0.82934** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Qualitative comparisons between IE, SSIM, PSNR, and CORR of different algorithms in outdoor experiment. The best results are written in bold text. In both experiments, we used an Intel Core i7 11700H CPU manufactured by Intel Corporation, headquartered in Santa Clara, CA, USA to process the data.