## 4 Experiments and Results
### Ablation Study  
To further investigate the effectiveness of the proposed method in this paper, we conducted ablation experiments for analysis. Tables 5 and 6 show the quantitative results for the SOTS test set and the HSTS test set, respectively. Figures 11 and 12 show the visual results on the SOTS test set and the HSTS test set, respectively. As can be seen in Figures 11 and 12, the combination of the feature fusion module and the multi-scale skip connection based on the residual network resulted in a clear and haze-free image with better visual effects. The multi-scale skip connection based on residual network inputted features from different scales in the encoding module to different layers in the decoding module to provide more and richer features of the original image for the decoding module, while the feature fusion module combined the rich features extracted from the encoding module and the features in the decoding module to allow the haze-free image to retain the original image features as best as possible in order to solve the problems of color distortion and lack of image detail features, and then obtain a clear and haze-free image with a good quantitative result and visualization effect.  
Figure 10: Visual results on real-world images: (**a**) hazy images; (**b**) DCP; (**c**) AOD-Net; (**d**) GCANet; (**e**) EPDN; (**f**) YOLY; (**g**) proposed method.  
Figure 13 shows the visualization results on the real-world haze image dataset. As can be seen from the first panel of Figure 13e, combining the feature fusion module and the multi-scale skip connection based on the residual network could effectively solve the color distortion problem. From the second panel of Figure 13e, we can see that the combination  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Components**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**SSIM**} \\ \hline
**FFM** & & & \\ \hline \(\times\) & \(\times\) & 22.13 & 0.887 \\ \(\surd\) & \(\times\) & 23.51 & 0.906 \\ \(\times\) & \(\surd\) & 23.28 & 0.908 \\ \(\surd\) & \(\surd\) & **24.20** & **0.916** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative results on the HSTS.  
Figure 11: Visual results on the SOTS dataset: (**a**) hazy images; (**b**) corresponding haze-free images; (**c**) no FFM and MSC; (**d**) FFM; (**e**) MSC; (**f**) FFM + MSC.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Components**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**SSIM**} \\ \hline
**FFM** & & & \\ \hline \(\times\) & \(\times\) & 22.13 & 0.887 \\ \(\surd\) & \(\times\) & 23.51 & 0.906 \\ \(\times\) & \(\surd\) & 23.28 & 0.908 \\ \(\surd\) & \(\surd\) & **24.20** & **0.916** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative results on the SOTS. FFM, and MSC denoting the feature fusion module and multi-scale skip connection based on the residual network, respectively. \(\surd\) indicates the presence of a structure and \(\times\) indicates the absence of a structure.  
of the feature fusion module and multi-scale skip connection based on the residual network could effectively solve the problem of image detail feature loss. Thus, it can be seen that the proposed structure in this paper not only performed well on synthetic datasets, but it also achieved good visualization results on real-world datasets.  
Figure 12: Visual results on the HSTS dataset: (**a**) hazy images; (**b**) corresponding haze-free images; (**c**) no FFM and MSC; (**d**) FFM; (**e**) MSC; (**f**) FFM + MSC.  
Figure 13: Visual results on real-world images: (**a**) hazy images; (**b**) no FFM and MSC; (**c**) FFM; (**d**) MSC; (**e**) FFM + MSC.