# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 4 Experiments and Evaluations  
### Experimental Dataset  
In most image-based fog research, datasets typically consist of pairs of foggy and fog-free images of the same scene, often created using artificially synthesized images. However, datasets specifically for fog density studies are relatively scarce compared to  
Figure 8: Example of patch size application using a blue frame. Figure 7: Comparison image when pixel value is set to 0: (**a**) Example of an image with pixel values of the top 0.1% set to 0; (**b**) Example of an image with peripheral patches of the top 0.1% set to 0.  
road image datasets. To address this limitation and advance fog density research in marine environments, this study introduces two types of real-time marine image datasets. Figure 9 Dataset (a) consists of images with minimal atmospheric light interference, captured from four different ports equipped with buoys. These ports were carefully selected to reduce the impact of atmospheric light, ensuring that high-quality data could be used for analysis. In contrast, Figure 10 Dataset (b) comprises images taken from several ports significantly affected by atmospheric light, intended for comparative analysis with Figure 9 Dataset (a). All datasets were captured in South Korea. The images in Dataset (a) shown in Figure 9 were taken from the ports of Ganghwa, Pyeongtaek, Baengryeongdo, and Jindo, marked with blue dots on the map. On the other hand, Figure 10 Dataset (b) includes images captured from Jeju Port and Mukho Port, marked with red dots on the map. The geographic locations of these ports are visually represented in Figure 11, helping to understand the distribution of the datasets. Figure 9 Dataset (a) contains a total of 320 images, each from one of the four mentioned regions. This dataset is accompanied by a detailed fog classification table categorizing the images based on visibility conditions. Images from each location are classified into four distinct fog intensity labels: No-fog (20 images), Low-fog (20 images), Mid-fog (20 images), and Dense-fog (20 images). These classifications are systematically presented in Table 1. In this study, the RDCP fog intensity criteria were adhered to, ensuring consistency with the established standards for fog intensity measurement. Furthermore, this approach involves removing the sky region to enhance the accuracy of fog density assessment. By applying these criteria, we aim to maintain a high level of precision in evaluating fog intensity, ensuring that our analysis results are both reliable and accurate. Figure 9 displays images from the Baengnyeongdo region in Dataset (a), categorized by fog intensity. In Figure 9, represents No-fog, represents Low-fog, represents Mid-fog, and represents Dense-fog. Figure 10 presents examples from Dataset (b), showcasing images from clear days with abundant ambient light, as seen in Figure 10a-j. In Figure 12a, it is evident that the day is clear with minimal influence from atmospheric light, showing almost no fog. On the other hand, in Figure 12b, you can see elements that could be mistaken for fog within the image due to the significant influence of atmospheric light. The results of this observation are represented as Dark Channel Prior (DCP) percentages in Table 2. As shown in the table, Dataset (a) has a DCP percentage of 63%, while Dataset (b) shows 40%, indicating that atmospheric light negatively impacts the camera's performance. Therefore, we proceed with an experiment comparing the RDCP and AEA-RDCP algorithms using Datasets (a) and (b). This setup allows for a compre \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sea Fog Intensity** & **Visibility (Meters)** & **DC Percent (\%)** \\ \hline No-fog & 1500– & 75–100 \\ Low-fog & 1000–1500 & 50–75 \\ Mid-fog & 500–1000 & 25–50 \\ Dense-fog & 0–500 & 0–25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sea fog strength classification table according to visibility. Figure 11: Location of the image sets: Blue for Dataset (a), Red for Dataset (b).