## 4 Experiment
### Ablation Experiments  
#### 4.4.1 Comparison of Different Modules  
To further verify the effectiveness of each module in the network, ablation experiments were designed with different model states. The results of the module effectiveness experiments are shown in Table 3, and the visualization results are shown in Figure 9. In the table, the base model is the CycleGAN; \"A\" is the improved generator structure based on the atmospheric scattering model, \"B\" is the improved loss function, and \"C\" is the FBCA module in the network. The comparison results are shown in Table 3. The visualization results are shown in Figure 10.  
As shown in Table 3, by improving the generator network structure, the PSNR and SSIM increase by 16.10% and 25.24%, respectively, indicating that after combining the atmospheric scattering model, the model can make full use of the physical characteristics of fog to better learn image features. The generator of the traditional CycleGAN is based on a residual network, which is more complex to calculate, while the improved generator greatly reduces the amount of computation, thus achieving better results in terms of FLOPs. After the introduction of the improved loss function, the performance of the model is further improved, indicating that the optimization of the loss function can better guide model training and optimize parameters. Compared with the original network, the PSNR increases by 32.10%, the SSIM increases by 31.07%, the information entropy increases by 4.79%, and the NIQE index is reduced by 20.1%, indicating that the quality of the dehazed image has been significantly improved. With the introduction of the FBCA module, the best test results were achieved, with a 32.10% increase in PSNR and a 31.07% increase in SSIM compared to the original network. The dehazed image processed by the model is clearer and more in line with human visual perception. Due to the introduction of the FBCA module, the proposed method results in a moderate increase in the indicators of Params and FLOPs. However, as demonstrated by the experimental results, this increase leads to significant improvements in performance, representing a reasonable trade-off between performance and computational cost. As shown in Figure 9(a), it can be observed that with the increase in training epochs, the PSNR of both methods shows an overall upward trend. However, FA-CycleGAN consistently achieves higher PSNR values across different stages and exhibits smaller fluctuations, indicating better stability and convergence. These results demonstrate that FA-CycleGAN is capable of generating higher-quality dehazed images, thereby validating the effectiveness of the proposed method in preserving image details and improving dehazing performance. As shown in Figure 9(b), both models exhibit a decreasing trend in loss as training progresses, indicating successful convergence. Notably, FA-CycleGAN maintains consistently lower loss values with smaller fluctuations throughout the training process and converges more rapidly compared to CycleGAN. This result further supports the effectiveness of FA-CycleGAN in achieving better stability and optimization during model training. From the visual effects shown in Figure 11, it can be observed that the original CycleGAN network's dehazing is not thorough, with \"pseudo-shadows\" present in some areas and the sky area exhibiting localized exposure. After improving the generators based on the atmospheric scattering model and loss function, the model can make full use of physical prior information; there are no longer any \"artifacts\", and the local exposure of the sky area is improved. This demonstrates that physical prior modeling effectively provides more reasonable guidance for dehazing. After introducing the FBCA module, the model can fully integrate the features of each level and assign different weights. The model exhibited significantly enhanced restoration capabilities in detailed regions (such as object edges and texture-rich areas).