## 4 Experiment
### Comparative Experiments
#### 4.3.2 Maritime Scene  
In this section, experiments are conducted using the MDD for training and testing. The results are compared with current classical and more cutting-edge dehazing algorithms. The comparison results are shown in Table 2. The visualization results are shown in Figure 9.  
In Table 2, it can be seen that our proposed network has relatively stable performance on MDD. The SSIM and PSNR values were 24.850 and 0.956, both achieving the highest scores. The PSNR and SSIM values can reflect the similarity between the dehazed image and the real image. The FBCA weighting mechanism proposed in this paper assigns higher weights to important features, retaining details while suppressing useless noise. Image structure information and details can be recovered better, thereby improving the PSNR and SSIM indicators. Information entropy reflects image complexity and information content. The maximum value of 7.433 is obtained in IE, indicating that the image, after using the model to remove fog, is clearer and more detailed. This improvement is due to the introduction of multi-scale feature fusion and a weighting mechanism in the model, which allows the model to effectively capture high-frequency information, thereby improving the clarity and information of the image. The NIQE index evaluates whether the image is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**Paired**} & \multicolumn{3}{c}{**Unpaired**} \\ \cline{2-7}  & **(a) FFANet** & **(b) C2 PNet** & **(c) MSBDN** & **(d) DCP** & **(e) CycleDehaze** & **(f) Ours** \\ \hline PSNR\(\uparrow\) & 16.437 & 19.061 & 13.296 & 15.497 & 13.196 & **24.850** \\ SSIM\(\uparrow\) & 0.747 & 0.862 & 0.423 & 0.744 & 0.730 & **0.956** \\ IE\(\uparrow\) & 6.997 & 7.278 & 7.271 & 6.895 & 6.564 & **7.433** \\ NIQE\(\downarrow\) & 2.522 & 2.491 & 2.543 & 2.580 & 2.557 & **2.431** \\ \hline \end{tabular}
\end{table}
Table 2: Comparison results of different models using MDD.  
Figure 9: (**Hazy**) and (**Clear**) are the maritime images of MDD dataset, and (**a–f**) are the dehazing experimental results of method FFANet, C2 PNet, MSBDN, DCP, Cycledehaze, and the proposed method.  
visually natural. The optimal value of the NIQE index indicates that the defogged image has recovered details and textures and is more in line with human visual perception.  
The visualization results are shown in Figure 9. MSBDN and CycleDehaze exhibited poor performance on the MDD dataset, resulting in a significant amount of haze residue in the dehazed images and incomplete dehazing, which indicates that their modeling ability for maritime scenes is insufficient because they cannot effectively distinguish between haze and image information. Since DCP is based on the dark channel prior method, it is prone to large-area color distortion in bright areas such as the sky, and there is a degree of image over-exposure. The two networks, FFANet and C2 PNet, perform relatively well, but there is some color distortion because the feature differences of different regions are not fully considered in the fusion process of feature extraction. In contrast, the deep learning model is used in the proposed method to extract the parameters of the atmospheric scattering model and refine them. At the same time, a multi-scale feature fusion and weighting strategy is introduced. This model can capture both global and local features and dynamically adjust the weights so as to maintain high dehazing performance in complex scenes, and the visual effect in the dehazed images is more natural.