# A Polarization-Based Method for Maritime Image Dehazing
## 4 Results and Discussion
### Subjective Evaluation  
As shown in Figure 8, the DCP method shows improved recovery of distant structures, such as the buildings in Figure 8a and the chimney in Figure 8c. However, it exhibits significant haloing and gray distortion, especially around the boundary between the sea and the sky and near the lighthouse. Gray distortion is noticeable in the sky region of Figure 8c and the hull of the ship in Figure 8a. The Retinex method results in overly bright images, leading to reduced contrast. While the BCCR method effectively removes haze from nearby objects, its performance on distant objects is worse, with residual haze present, as seen in the distant boat in Figure 8b and the distant buildings in Figure 8a.  
In Figures 9 and 10, specific regions from Figure 8a,c were selected for magnification processing to examine detailed local information. A comparative evaluation was conducted between the established DCP and BCCR methods and the newly proposed method, with the new method exhibiting effectiveness, as shown in Figure 8. In region a-1, the DCP method displays prominent halo effects near the lighthouse, affecting the retrieval of background structure details. The BCCR method shows limited success in recovering distant buildings. Conversely, our approach effectively restores details in remote buildings, with appropriate luminance levels. In region a-2, where buildings are barely distinguishable, the DCP method can partially restore some details, but halos produced by the ship obscure certain details and reduce image brightness. While the BCCR method can enhance image brightness, its ability to recover details is restricted. Our method reveals distant windows and building edges, with suitable brightness, effectively restoring building details. In regions b-3 and b-4, the DCP method excels in detail recovery but generates halos along mountain edges. The BCCR method enhances image brightness but lacks detail recovery. In contrast, our method successfully achieves robust detail recovery without inducing halos, and notably, it provides a contrast that surpasses that of the aforementioned methods.  
Figure 9: S\({}_{0}\) images and zoom images from region a-1 (**a,b**).  
Both the DCP and BCCR methods utilize the dark channel prior theory, which requires at least one channel of the three to have pixel values close to zero. However, this theory may not be applicable to sea fog images with large sky regions, leading to the inaccurate estimation of \(A_{\infty}\). Additionally, in obtaining the dark channel map, DCP disrupts the edges of the image transitions, resulting in inaccurate estimation of the transmittance map of the atmosphere tending to include blocky and halo effects. The Retinex method, due to its adoption of grayscale assumptions, leads to low-contrast images. Our method utilizes an abundance of polarization information (the angle of polarization, the degree of polarization, polarization difference image), which is superior in information dimensionality compared to other methods. Moreover, our method is specifically designed for the distinctive scenario of sea fog. By using polarization difference information, we can effectively estimate a suitable \(A_{\infty}\). The combined use of the angle of polarization and the degree of polarization can better estimate the transmission map of the atmosphere.  
The gray level histogram functions as a statistical representation of the distribution of pixel intensities in an image, offering a visual portrayal of the improvement in a foggy image. As shown in Figure 11, gray level histograms were computed for different methods applied to the images shown in Figure 8b. A wider histogram indicates higher image quality and greater detail. The gray level histogram of our proposed approach exhibits superior efficacy when compared to alternative methods.