# A Polarization-Based Method for Maritime Image Dehazing
## 3 Experiments and Proposed Method
### Separate Seawater Area  
The polarimetric dehazing model operates under the assumption that direct light lacks polarization characteristics. However, research has shown that seawater exhibits significant polarization phenomena [24; 25]. In fact, when objects with specular surfaces, such as glass or water, are illuminated, the direct light becomes polarized [21; 26; 27]. When objects with specular surfaces, such as glass or water, are illuminated, the direct light becomes polarized. This effect is particularly prominent in maritime environments, where vast bodies of water are closer to the detector than the target objects. Consequently, the polarized component of the air light in these aquatic regions is notably diminished compared to that of direct light. The presence of a large \(A_{P}\) in water regions, as denoted by Equation (6), results in an overestimation of parameter \(A\), leading to the formation of dark patches in the dehazed image. Figure 5 illustrates the impact of utilizing the aforementioned polarimetric dehazing model in Section 2.2. To address this issue, a methodology is proposed for segmenting the sea surface and mitigating the influence of air light in aquatic areas. The initial step involves segmenting the seawater area, followed by the introduction of a bias coefficient \(\epsilon_{2}\) to regulate \(A\) in Equation (16). The detailed operations are described in the following paragraphs. The technique of image segmentation through thresholding involves the categorization of pixels into separate classes based on a predefined grayscale threshold. Pixels with grayscale values falling within a specific range are grouped together. Improved segmentation results in a more pronounced differentiation in characteris  
Figure 5: The dehazing image obtained without using the segmentation seawater method. components. The Otsu algorithm [28], a well-known method for thresholding segmentation, is widely utilized due to its reliability. Also known as the maximum between-class variance method, the Otsu algorithm divides an image into background and target segments by examining grayscale properties. A higher between-class variance indicates greater distinctions between the two segments and a lower likelihood of misclassification. The calculation process of between-class variance can be summarized by the following formulas:  
\[\mu_{h}=R_{1}\times\mu_{1}+R_{2}\times\mu_{2} \tag{13}\]  
\[\sigma_{1}^{2}=R_{1}(\mu_{1}-\mu_{h})^{2}+R_{2}(\mu_{2}-\mu_{h})^{2}=R_{1}R_{2} (\mu_{1}-\mu_{2})^{2} \tag{14}\]  
\[\sigma_{1}^{2}(k^{*})=\max_{0\leq k<M}\sigma_{1}^{2}(k) \tag{15}\]  
where it is assumed that the image has \(M\) grayscale levels, with \(k\) as the classification threshold, and the total average grayscale value of the image is \(\mu_{h}\). The background portion consists of pixels with grayscale values ranging from \(0\) to \(k\), with the proportion of these pixels to the total number of pixels denoted as \(R_{1}\), and their average grayscale value as \(\mu_{1}\). The target portion consists of pixels with grayscale values ranging from \(k\) to \(M-1\), with the proportion of these pixels to the total number of pixels denoted as \(R_{2}\), and their average grayscale value as \(\mu_{2}\). Substituting Equation (13) into Equation (14), we obtain the between-class variance \(\sigma_{1}^{2}\). When the absolute difference between the average grayscale values of the background and target in Equation (14) is maximal, the between-class variance is maximal. As shown in Equation (15), the obtained classification threshold \(k\) is regarded as the optimal threshold \(k^{*}\), representing the best segmentation result. By iterating over the values of \(k\) in the range from \(0\) to \(M-1\), the optimal threshold value \(k^{*}\) can be obtained. The degree of polarization can indicate specific physical properties of objects, with objects composed of different materials exhibiting varying degrees of polarization.