## 3 Results
### Comparative Experiments  
To evaluate our proposed model more accurately, a comparison with state-of-the-art dehazing methods was performed on icing wind tunnel haze images. Advanced methods include traditional dehazing methods: DCP [2], CAP [3], AMEDF [24], LBF [25]; and deep learning methods: AOD-NET [26], MSCNN [6], FFA-Net [11], D4 [27].  
Table 2 shows the comparison results between our model and traditional dehazing methods on the wind tunnel dataset, and Figure 6 shows the corresponding visual comparison. From Figure 6, we can visually observe that DCP, CAP, and LBF have color distortion, and the colors of the dehazed images are very dark. AMEF has a certain dehazing effect, but there is still some residual haze in the dehazed image. According to Table 1, although traditional dehazing methods obtain a higher number of visible edges in the dehazed result, the value of \(\overline{r}\) needs to be improved. Our model achieves the best result of \(\overline{r}\) in dense fog situations with an LWC of 1.31 and 1.0, and the value of \(NIQE\) is also smaller. Overall, our model dehazes more thoroughly and has better visual effects.  
Table 3 shows the comparison results of our model and deep learning-based methods on the wind tunnel dataset, and the visual comparison of the results after dehazing is shown in Figure 7. From the results, it can be observed that AOD-NET also suffers from color distortion, resulting in darkened colors in the dehazed images. MSCNN has poor dehazing performance, D4 performs well in dehazing the foreground of the images, while FFA-Net destroys the images. In contrast, our model not only has a certain restoration effect on the distant parts of the images, but also better restores the color of the haze-free images. The dehazed images are clearer, and the original contours are preserved. Therefore, a smaller \(NIQE\) value is obtained.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **Image** & **DCP**[2] & **CAP**[3] & **AMEDF**[24] & **LBF**[25] & **Ours** \\ \hline \multirow{6}{*}{\(e\)} & Image 1 & 1.67 & 3.51 & 1.59 & 1.81 & **2.03** \\  & Image 2 & 6.73 & 14.64 & 4.54 & 11.80 & **3.47** \\  & Image 3 & 1.33 & 3.36 & 1.41 & 2.52 & **1.31** \\  & Image 4 & 1.21 & 2.35 & 1.18 & 1.80 & **1.82** \\  & Image 5 & 2.60 & 3.12 & 2.41 & 4.41 & **0.64** \\  & Image 6 & 3.74 & 4.98 & 2.48 & 4.41 & **0.52** \\ \multirow{2}{*}{Average} & & 2.88 & 5.33 & 2.27 & 4.46 & **1.63** \\ \hline \multirow{6}{*}{\(\overline{\tau}\)} & Image 1 & 1.22 & 1.21 & 2.35 & 1.19 & **4.45** \\  & Image 2 & 1.16 & 1.46 & 2.48 & 1.84 & **5.34** \\  & Image 3 & 1.04 & 1.17 & 2.50 & 1.24 & **3.82** \\  & Image 4 & 1.13 & 1.11 & 2.49 & 1.17 & **4.03** \\  & Image 5 & 1.34 & 1.50 & 2.06 & 1.94 & **2.28** \\ \multirow{2}{*}{Average} & Image 6 & 1.24 & 1.24 & 2.23 & 1.47 & **1.79** \\  & & 1.19 & 1.28 & 2.35 & 1.48 & **3.62** \\ \hline \multirow{6}{*}{\(NIQE\)} & Image 1 & 3.43 & 7.48 & 3.45 & 7.48 & **3.17** \\  & Image 2 & 2.80 & 3.33 & 2.81 & 3.33 & **2.86** \\ \multirow{2}{*}{\(NIQE\)} & Image 3 & 3.65 & 4.21 & 3.30 & 4.21 & **3.03** \\  & Image 4 & 3.31 & 4.22 & 3.10 & 4.22 & **2.95** \\ \multirow{2}{*}{\(NIQE\)} & Image 5 & 3.04 & 2.84 & 2.88 & 2.84 & **2.54** \\  & Image 6 & 3.34 & 3.58 & 3.70 & 3.58 & **3.11** \\ \multirow{2}{*}{Average} & & 3.26 & 4.28 & 3.21 & 4.28 & **2.94** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the results of traditional methods on the test set.  
Figure 6: Comparison of six real foggy images in an icing wind tunnel with existing traditional methods. (**1,2**) LWC = 1.31; (**3,4**) LWC = 1.0; (**5,6**) LWC = 0.5.