# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 4 Experiment Results and Discussions
### Dehazing Experiment
#### 4.1.2 Performance Evaluation  
The evaluation metrics applied in the dehazing experiment adopt _PSNR_ and _SSIM_ Index. The _PSNR_ metric, which can quantify the reconstruction quality of the restored image, is based on the Mean Squares Error (_MSE_) between the ground truth image \(y\) and the restored image \(x\). For an 8-bit RGB image, the formula for _PSNR_ is as follows:  
\[ \begin{split} MSE(x,y)=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1 }^{W}\left\|y_{i,j}-x_{i,j}\right\|_{2}^{2},\\ PSNR=10\cdot\log_{10}\left(\frac{255^{2}}{MSE(x,y)}\right),\end{split}  \tag{10}\]  
where \(H\) and \(W\) are the height and width of the image, \(y_{i,j}\) and \(x_{i,j}\) are the pixel values of the ground truth image and the restored image. On the flip side, the _SSIM_ metric can approximate the luminance, contrast and structure of the restored image compared to the ground truth image. The formula for _SSIM_ is as follows:  
\[ \begin{split} SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{ xy}+C_{2})}{(\mu_{x}^{2}+\mu_{y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})}. \end{split}  \tag{11}\]  
where \(\mu_{x}\), \(\mu_{y}\) are the mean values of the restored image and the ground truth image, \(\sigma_{x}^{2}\), \(\sigma_{y}^{2}\) are the variances of the restored image and the ground truth image, \(\sigma_{xy}\) is the covariance of the restored image and the ground truth image, \(C_{1}\) and \(C_{2}\) are constants to stabilize the division with weak denominator. In addition to the _PSNRLoss_, it can be calculated as:  
\[ \begin{split} PSNRLoss=-PSNR(x,y).\end{split}  \tag{12}\]  
To evaluate the dehazing performance of our algorithm, we compared it with five other state-of-the-art (SOTA) algorithms based on four quantitative metrics: _PSNR_, _SSIM_, model parameters, and multiply-accumulate operations (MACs). The quantitative evaluation comparison is shown in Table 4.  
In Table 4 of the quantitative metrics, our model achieved the best image quality indicators and minimized model parameters and memory consumption. Specifically, our model achieved a _PSNR_ of 30.18 dB and an SSIM of 0.972, comparing DehazeFormer [10] with reducing 29.44% #Param and 26.27% memory computational cost as well as increasing 1.07 points in _PSNR_. The qualitative comparison of the dehazing effect is shown in Figures 12 and 13. The results show that our model can effectively remove the haze in the image and restore the clear image, with the best visual effect among the six algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & _PSNR_ & _SSIM_ & _Param (M)_ & _MACs (G)_ & _Latency-512 x 512 (ms)_ \\ \hline hazed & 13.98 & 0.635 & - & - & - \\ DehazeNet [20] & 20.15 & 0.863 & **0.009** & **0.581** & **1.34** \\ GridDehazeNet [36] & 26.26 & 0.948 & 0.956 & 21.49 & 12.65 \\ GCANet [37] & 24.33 & 0.921 & 0.702 & 18.41 & 3.69 \\ PFDN [38] & 28.97 & 0.961 & 11.27 & 50.20 & 7.31 \\ DehazeFormer [10] & 29.11 & 0.963 & 0.686 & 6.66 & 11.24 \\ ours & **30.18** & **0.972** & 0.484 & 4.91 & 8.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of various models on dehazing performance metrics, model parameters, memory consumption and latency. In addition, we conduct ablation experiments to verify the effectiveness of the network modules used in this study and the impact of the image augmentation methods used during training on the training metrics of the network model. In the ablation experiment, we replaced the MixAttention and the Mobilevit3 module used in this paper with conventional depthwise separable convolutions and the transformer module used in [10], respectively, while also using no image segmentation and image augmentation operations on the dataset as the baseline. The result is shown in Table 5.  
According to the ablation experiment result of Table 5, after replacing conventional depthwise separable convolutions with the MixAttention module, the _PSNR_ increased by 0.52 dB.