# Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition
## 3 Results and Discussions
### Comparisons of Image Enhancement Methods  
In this paper, we compare the proposed algorithm with identical algorithms specially designed for image enhancement through fog, including CLAHE, the SSR, and the Adaptive Multi Scale Retinex (AMSR) [33]. For CLAHE, the clip limit for normalized images is set to 0.02, with image segmentation into 8 \(\times\) 8 blocks. The SSR uses a Gaussian function of standard deviation set to 50 pixels. The experimental data from the fog chamber and the comparison results are illustrated in Figure 5, where Figure 5a-f depict multiple targets inside the fog chamber. The upmost row shows data captured in clear conditions. The second to sixth rows, respectively, showcase the original low visibility image, the outcomes of CLAHE, the SSR, the AMSR, and HMHE. The data analysis indicates that, in indoor environments characterized by severe uneven lighting, CLAHE struggles with significant light intensity variations between blocks, resulting in conspicuous block artifacts due to its inability to harmonize the light intensity across different tiles effectively. The SSR, which incorporates homomorphic filtering, successfully recovers image information; however, it yields a subdued contrast that obscures the visibility of the signal. AMSR shows performance comparable to our algorithm. Yet, CLAHE, the SSR, and the AMSR overamplifies the noise in the high frequency domain. Quantitative evaluations are presented in Table 1, corresponding to Figure 5. The SSIM, FSIM and CORR here are calculated by using the non-fogged images and their resulting enhanced fogged images. Thus, these values directly reflect the resemblance of the output images and the non-fogged images. The overall numerical results are generally smaller than 0.8, since in non-fogged conditions, the inhomogeneous illumination can hardly affect the perception-desired objects. However, under a fogged environment, especially extremely low visibility conditions, the illumination tends to hugely affect the low-frequency component of the image. The best result of each scenario is written in bold text in Table 1. Table 1 demonstrates that, while the algorithm proposed in this work does not always outperform competing methods, it achieves competitive results across various metrics. For quantitative analysis, the majority of the index of our algorithm is higher. However, in Figure 5d,e, the SSR exhibits a better score. This result is of an inadequate exposure of the clear images in both scenarios, which coincidently aligns with the dark result coming from the SSR. In normal cases with appropriate exposure-time for clear images, HMHE always exhibits higher scores. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Figure** & **IQA** & **CLAHE** & **SSR** & **AMSR** & **Ours** \\ \hline \multirow{4}{*}{Figure 5a} & IE & 7.45209 & 6.85442 & 6.73334 & **7.75753** \\  & SSIM & 0.08023 & 0.10667 & 0.08196 & **0.19229** \\  & FSIM & 0.42930 & 0.47389 & 0.47204 & **0.48000** \\  & CORR & 0.11995 & 0.14259 & 0.13011 & **0.17085** \\ \hline \multirow{4}{*}{Figure 5b} & IE & 7.38049 & 6.74672 & 6.70964 & **7.73492** \\  & SSIM & 0.08243 & 0.07527 & 0.06774 & **0.31135** \\  & FSIM & 0.60550 & 0.59779 & 0.59932 & **0.61148** \\  & CORR & 0.23337 & 0.12780 & 0.13975 & **0.26364** \\ \hline \multirow{4}{*}{Figure 5c} & IE & 7.33908 & 6.75464 & 6.70898 & **7.61570** \\  & SSIM & 0.07678 & 0.07979 & 0.08278 & **0.29756** \\ \cline{1-1}  & FSIM & 0.59185 & 0.60162 & 0.61475 & **0.62179** \\ \cline{1-1}  & CORR & 0.16030 & 0.09169 & 0.10638 & **0.27352** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Qualitative comparisons between IE, SSIM, PSNR, and CORR of different algorithms in indoor experiment. The best scores are written in bold text. Figure 5: Comparison of different image enhancement algorithms in extremely low visibility condition inside the fog chamber. From left to right are clear image, original image, CLAHE, SSR, AMSR, and our algorithm, respectively. (**a–f**) demonstrates different targets.