# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 2 Literature Review  
### Visibility Estimation Studies  
The research on fog has largely been concentrated on terrestrial environments, with considerable effort dedicated to developing fog removal techniques rather than methodologies for visibility estimation. Fog removal on land is critical across various domains such as traffic safety, architectural applications, and environmental monitoring. When fog is present, visibility can be severely limited, which poses risks in multiple scenarios including driving, construction, and environmental assessment. Addressing these issues is particularly important in fields where safety and operational efficiency are paramount. Similarly, in the maritime industry, precise real-time visibility estimation is as crucial as the fog removal techniques used on land. Limited visibility due to fog can lead to maritime accidents, highlighting the need for accurate real-time visibility information to ensure safety and efficiency in marine operations. Therefore, reliable visibility estimation technologies are essential for effectively managing these challenges [15; 16; 17; 18; 19]. In this context, [PERSON] proposed an innovative method for estimating fog density using the Color Attenuation Prior (CAP) technique [20]. This method involves calculating the color attenuation ratio for each pixel in an RGB image to determine the fog density. Typically, objects that are farther away in a foggy image experience greater color attenuation, which allows for an estimation of fog density based on these variations. The CAP algorithm is capable of rapidly and efficiently determining fog concentration, making it useful in various fields where fast and accurate fog density estimation is required. Additionally, [PERSON] conducted research to address visibility issues caused by fog by analyzing video surveillance footage from airports [21]. This study developed a sophisticated model using deep-learning algorithms to accurately estimate visibility under foggy conditions. The model was integrated into airport video surveillance systems to provide real-time visibility monitoring, ensuring consistent performance across different weather conditions and enhancing overall airport safety and operational stability. In the realm of fog-related research, the DCP algorithm has made significant contributions [14; 15]. The DCP algorithm has been extensively used in dehazing research to restore images where visibility has been compromised due to fog. It has also been applied to visibility estimation studies. The DCP algorithm is based on the empirical observation that, in fog-free images, at least one of the color channels--Red (R), Green (G), or Blue (B)--typically exhibits very low intensity values. This principle is known as the DCP, and the channel with the lowest intensity value is referred to as the DC. The mathematical expression for this principle is as follows in Equation (1):  
\[J^{dark}(x)=\min_{y\in\Omega\{x\}}(\min_{c\in\{r,g,b\}}J^{c}(y)), \tag{1}\]  
where \(J^{c}\) represents the color channel, and \(\Omega(x)\) denotes a local patch of \(n\times n\) pixel centered at \(x\). The pixel \(y\) is within this local patch. The DCP algorithm can be used for fog removal or intensity estimation. The main processes of the DCP algorithm include the following:  
1. Generation of the DC Image: Creating the DC image using the minimum values per channel from local patches;
2. Estimation of Atmospheric Light: Calculating the atmospheric light value using the brightest pixels in the DC image;
3. Estimation of the Transmission Map: Calculating an initial transmission map, then refining it to obtain the final transmission map;
4. Calculation of the Restored Image: Using the transmission map and atmospheric light value to restore the original image. [PERSON] [22] proposed an enhanced method by combining the DCP with distance information from fixed objects in coastal areas, aiming to estimate comprehensive visibility over extensive regions, such as ports.