# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
## 4 Experiments and Results
### Comparisons with State-Of-Art Methods  
**Results of the synthesis dataset. ** We recorded the results of our method and other advanced methods using the SOTS [32] test set, as shown in Table 1.  
For the synthesis dataset of SOTS [32], our network achieved a good performance with respect to both the PSNR and the SSIM. Table 1 demonstrates that our method performed best using the indoor dataset of SOTS, and produced increases of 1.22 dB in the PSNR and 0.01 in the SSIM compared with the second most successful method. For the outdoor dataset of SOTS, our method achieved the best performance in terms of PSNR and ranked second in terms of SSIM compared with other methods. In Figure 6, we show three examples of SOTS [32]. All of the methods were shown to be effective on this dataset, but DCP [3] rendered recovered images that were too heavy in color and also generated artifacts in the images, which led to blurriness. AOD-Net [16] brought out deep color, and DehazeNet [15] gave the images high contrast. O-HAZE and I-HAZE use hazy images generated by a professional haze generating machine. Table 2 shows the results of the average of the PSNR and SSIM values, and we display some sample results in Figure 6. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Method**} & **DCP** & **DehazeNet** & **AOD-Net** & **cGAN** & **DCPDN** & **Ours** \\ \hline \multirow{2}{*}{Indoor} & PSNR & 18.05 & 22.36 & 19.78 & 21.02 & 18.22 & 23.58 \\  & SSIM & 0.817 & 0.844 & 0.887 & 0.839 & 0.815 & 0.897 \\ \hline \multirow{2}{*}{Outdoor} & PSNR & 18.74 & 22.57 & 21.12 & 20.35 & 19.95 & 23.06 \\  & SSIM & 0.823 & 0.852 & 0.897 & 0.855 & 0.842 & 0.878 \\ \hline \multicolumn{2}{l}{DCP: Dark Channel Prior; AOD-Net: ALL-in-One Dehazing Network; cGAN: Conditional Generative Adversarial Network; DCPDN: Densely Connected Pyramid Dehazing Network.} & & & & \\ \end{tabular}
\end{table} 
Table 1: Average Peak Signal-to-Noise ratio (PSNR) and Structural Similarity Index Metric (SSIM) results on SOTS (Synthesis Object Testing Set). (red font indicates the highest value, followed by blue font). **Results using real-world images. ** The visual comparison results of the images obtained by these methods using real hazy images can be seen in Figure 7. Several observations can be made: (1) Our method effectively removes the haze of real haze images, even during training on a synthetic dataset, thereby proving the robustness of the method; (2) DCP [4] causes color distortion in the sky area, but our method does not have this problem, instead eliminating the negative effects caused by DCP [4]; and (3) DehazeNet [15] and AOD-Net [16] are poor dehazing methods, whereas DCPDN [23] and cGAN [28] cannot effectively eliminate haze in images with dense fog. The method we propose demonstrates better visual effects. As shown in Figure 7, DCP cannot properly handle the area of sky in the hazy image, therefore, it is likely to generate artifacts during the dehazing progress, as seen in the 1 st, 2 nd and 4 th images in Figure 7. Also, heavy artifacts exist around the ground in the 7 th image in Figure 7. DehazeNet [15] blurs the background in some images, such as in the 3 rd, 4 th and 6 th images in Figure 7. AOD-Net [16] performs well on a synthesis dataset, creating a low brightness image, causing the foreground of these images to fade, as observed in the 7 th and 8 th images of Figure 7. The colors of the image restored by cGAN and by our method are more distinct than other methods, as shown in Figure 7, but our method is demonstrably better than cGAN in terms of prospective image recovery; our method is more realistic and effective in color and contour restoration.