# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 2 Literature Review
### Effects of Atmospheric Light on Visibility Estimation  
Research on visibility estimation has employed various methodologies, each with distinct advantages and limitations. A recurring challenge across these studies is the accurate differentiation of effects caused by environmental factors such as sunlight, which can manifest as shadows, reflections, or highlights in images. These effects often confound the algorithms' ability to precisely identify and quantify fog or haze [14; 26; 27; 28].  
The CAP algorithm [21], for instance, is effective for estimating fog density by analyzing the attenuation of color in RGB images. However, it struggles with distinguishing between the effects of fog and those caused by lighting variations, particularly those induced by sunlight. Sunlight can increase the overall brightness of an image, create intense highlights, and cast shadows, all of which exhibit characteristics that differ from the color attenuation due to fog. These lighting effects can lead to erroneous fog detection and miscalculations of fog density, as CAP is not designed to account for these nuances.  
Deep-learning-based approaches for visibility estimation also encounter significant challenges [22]. These models are highly dependent on the quality and diversity of their training data. If the training datasets are limited to specific geographic regions or weather conditions, the models may lack the generalization capability needed to accurately perform in different environments. Furthermore, adverse weather conditions such as strong light, shadows, or rain can further degrade the accuracy of these models, as they may not be adequately represented in the training data.  
Similar challenges are present in visibility estimation algorithms utilizing the DCP [13]. For instance, [PERSON]'s study highlighted that strong sunlight could cause certain areas of the image to become overly bright, complicating the accurate calculation of the DC [22]. This excessive brightness can obscure the true nature of the fog, leading to the underestimation of its intensity. [PERSON]'s research also pointed out that glare from sunlight could distort the image contrast, affecting the Grayscale Image Entropy (GIE) values used for visibility estimation, thereby reducing the algorithm's accuracy [23].  
Figure 3: Example of an image with the sky region removed: (**a**) Non-Crop Image; and (**b**) Crop Image.  
The RDCP approach attempts to mitigate some of these issues by focusing on the distribution of DC values below a specific threshold and excluding the sky region from the analysis [11]. This refinement aims to reduce the influence of sunlight and other bright light sources, which can create misleading signals in the data. However, as illustrated in Figure 4, areas affected by strong sunlight or other external light sources can still introduce inaccuracies in the calculation of the DC. These light sources can elevate the intensity levels in certain areas, mimicking effects typically associated with fog or haze [29].  
Despite the exclusion of the sky region, there remains a significant need to further mitigate the influence of atmospheric light to enhance the accuracy of sea fog detection. Therefore, this paper proposes additional refinements to the RDCP algorithm. Specifically, it suggests the exclusion of pixels influenced by atmospheric light from the DC calculation. This refinement aims to minimize the impact of extraneous lighting conditions, thereby improving the precision of visibility estimation and fog detection. By refining the RDCP algorithm in this manner, the study seeks to provide more reliable and accurate visibility assessments, particularly in complex maritime environments where atmospheric conditions can vary greatly.