## 4 Experiment
### Ablation Experiments
#### 4.4.2 Comparison of Different Attention Mechanisms  
In this section, ablation experiments on different attention mechanisms in the FBCA module were conducted. The impact of these attention mechanisms on model performance is evaluated through comparison. The OTS dataset is used as the training set, and the SOTS-outdoor dataset is used as the validation set to verify the effectiveness of the selected mechanisms. Specifically, the FBCA module without attention mechanisms is labeled as \"None\". The names of the introduced attention mechanisms are used as module names, including the channel attention mechanism, Convolutional Block Attention Module (CBAM) mechanism, ShuffleAttention mechanism, SKAttention mechanism, and CA mechanism used in this paper. The quantitative evaluation results are shown in Table 4, and the visual results are shown in Figure 12. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **(a) None** & \
\begin{tabular}{c} **(b) Channel** \\ **Attention** \\ \end{tabular} & **(c) CBAM** &
\begin{tabular}{c} **(d)** \\ **ShuffleAttention** \\ \end{tabular} & **(e) SK** & **(f) Ours** \\ \hline PSNR\(\uparrow\) & 23.069 & 22.091 & 25.679 & 25.358 & 24.819 & **26.248** \\ SSIM\(\uparrow\) & 0.903 & 0.879 & 0.930 & 0.921 & 0.918 & **0.945** \\ IE\(\uparrow\) & 7.115 & 7.219 & 7.373 & 7.362 & 7.296 & **7.433** \\ NIQE\(\downarrow\) & 2.879 & 2.697 & 2.491 & 2.524 & 2.452 & **2.431** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Objective comparison results of different attention mechanisms. Figure 11: (**Hazy**) and (**Clear**) are the images of SOTS-outdoor dataset, and (**a–e**) are the visualization results of different ablation modules. Figure 12: (**Hazy**) and (**Clear**) are the images of SOTS-outdoor dataset, (**a**) is the dehazing result without introducing the attention mechanism, and (**b–f**) are the dehazing results of attention mechanism channel attention, CBAM, shuffle attention, SK attention and ours. In Table 4, it can be seen that when only the channel attention mechanism is used, the PSNR, SSIM, and IE metrics decreased by 4.24%, 2.66%, and 6.32%, respectively. This occurs because channels are weighted by the channel attention mechanism based on their importance, which may lead to some information being weakened or lost. In the dehazing task, information from each channel may be crucial for the quality of the recovered image. Excessive weakening of certain channels can lead to performance degradation. The introduction of the CBAM, ShuffleAttention, SKAttention, and CA attention mechanisms resulted in improved model evaluation metrics. After the introduction of the CA attention mechanism, all indicators of the model increased by 13.78%, 4.65%, 4.47%, and 15.56%, respectively, achieving optimal performance. CBAM and SKAttention can combine spatial and channel attention mechanisms, enabling comprehensive modeling of feature maps and enhancing the model's understanding of input data. A non-linear channel shuffling operation was introduced in ShuffleAttention. By enhancing information interaction between different channels, it better utilizes the correlation and common features among various channels, enabling the model to learn more complex feature representations. The CA attention mechanism can embed positional information into channel attention. Weights are dynamically calculated based on coordinate positions rather than relying on fixed parameters for specific positions. Compared to other mechanisms, CA can consider the positional information of the entire feature map simultaneously, without being influenced by local regions. This helps the model comprehensively understand the entire input data, leading to better performance. All indicators have improved. In addition, according to the visualization effect shown in Figure 12, when the attention mechanism is not introduced, there is a problem of incomplete dehazing and poor visual quality. When other attention mechanisms are used, there is color distortion in the sky area and incomplete dehazing in some areas.