# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 4 Experiment and Results
### Experimental Results  
In this section, we apply the dehazing model to the hazy multispectral images captured by Sentinel-2. We also compare our model with the traditional method DCP [12] and four neural network methods, DehazeNet [14], AOD-Net [16], GridDehazeNet [19] and MSBDN [43]. For each hazy image used in the experiment, the corresponding clear sky image within a week was collected and used as the haze-free image for reference. Figure 8 demonstrates the dehazing results of nine bands. It can be observed that the visible light as well as Band 5, Band 6 and Band 7 have highly interfered with haze. The restoration results of DCP, DehazeNet and AOD-Net have haze residues. GridDehazeNet, MSBDN and our proposed method have no haze residue after dehazing. MSBDN has a certain color distortion. Infrared bands (Band 8, Band 11 and Band 12) are less interfered with haze. All of the methods achieve great recovery effects visually. For the visible light bands, the most interfered bands, the dehazing effect of DCP, DehazeNet and AOD-Net is relatively poor. MSBDN has a certain color distortion, while GridDehazeNet and our proposed method have better fidelity. Figure 7: Training loss curve. Tables 2-4 demonstrate the performance evaluation using PSNR, SSIM and FSIM. It can be observed that the results are basically consistent with the visual effects. Meanwhile, our proposed method significantly outperforms GridDehazeNet and MSBDN, which have better dehazing effects. It indicates that the method in this paper can better maintain ground details and color fidelity. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Image** & **DCP** & **DehazeNet** & **AOD-Net** & **GridDehazeNet** & **MSBDN** & **Proposed** \\ \hline Band 5 & 17.316 & 17.283 & 19.233 & 25.311 & 20.103 & 29.651 \\ Band 6 & 18.673 & 18.074 & 20.206 & 25.682 & 22.572 & 28.304 \\ Band 7 & 19.325 & 18.792 & 21.461 & 26.104 & 24.305 & 27.342 \\ Band 8 & 21.353 & 20.386 & 22.718 & 25.933 & 26.438 & 31.541 \\ Band 11 & 22.176 & 20.850 & 21.648 & 26.452 & 24.349 & 29.072 \\ Band 12 & 21.981 & 21.098 & 20.052 & 26.939 & 25.406 & 27.508 \\ Visible true color & 15.683 & 14.532 & 19.797 & 24.261 & 23.821 & 30.106 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results on different bands: PSNR. Figure 8: The dehazing effect on the hazy multi-spectral images captured by Sentinel-2. In this study, we conducted dehazing experiments on different cases of hazy images. Figure 9 shows the case of slightly hazy images. The results are the dehazing of visible true colors (Band 2, Band 3 and Band 4). It can be observed that haze is unevenly distributed, while haze shadows also exist in Figure 9A(a). The traditional method DCP works effectively in the area with a uniform haze distribution but not in the non-uniform part nor the haze shadow area. DehazeNet and AOD-NET also have the same problem with DCP. GridDehazeNet and MSBDN can effectively remove the haze effect, but MSBDN has a certain color distortion after restoration. Compared with these two methods, our proposed method shows outstanding performance in maintaining color and ground details. Figure 10 shows the visible true color dehazing result for an image with moderate haze interference. It can be observed that the results are similar to Figure 8. GridDehazeNet and MSBDN have removed haze, but they have also lost ground details to a certain extent. The method we propose maintains the ground details in a better way. Figure 11 shows the visible true color dehazing results of an image with heavy haze. DCP, DehazeNet and AOD-NET demonstrate poor restoration. MSBDN has a poor effect on maintaining the details of ground objects in Figure 11A(f), and there is still residual haze in the upper part in Figure 11B(f). GridDehazeNet shows great restoration, but there is still a certain loss in the ground details in Figure 11A(e). There are also some haze residues in Figure 11A(e), and there is some color distortion.