# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 4 Experiments and Discussions
### Results for Synthetic Non-Uniform RS Hazy Images  
We used the SateHaze1k [39] dataset, a synthetic non-uniform hazy dataset containing 1200 pairs of hazy/clean RS images with three haze densities--thin, moderate, and thick--for non-uniform dehazing evaluation. We randomly selected 30 hazy images from each haze density, resulting in a total of 90 hazy images as our test dataset. Figure 5 presents the comparative dehazing results for SateHaze1k. Figure 5a displays the input hazy images with different haze densities labeled by red fonts. Figure 5b-n show the dehazed results using different methods, and Figure 5o provides the ground truth for reference. As  
Figure 3: The results on real-world aerial RS hazy images. (**a**) Hazy image; (**b**) _DCP_[8]; (**c**) CEEF [6]; (**d**) FADE [41]; (**e**) DehazeFlow [42]; (**f**) SRKTDN [43]; (**g**) DWGAN [44]; (**h**) RefineDNet [17]; (i) USIDNet [45]; (**j**) ZID [14]; (**k**) DDIP [35]; (**l**) YOLY [13]; (**m**) ZIR [16]; (**n**) Ours; (**o**) Photoshop. shown in Figure 5, most dehazing methods can effectively remove thin haze, except for DehazeFlow (Figure 5e) and ZIR (Figure 5m). However, ZID produces over-enhancement and color distortion. Among traditional dehazing methods, _DCP_, CEEF, and FADE show similar performance in processing images with different haze densities, but FADE exhibits over-enhancement in Figure 5d. For thick hazy images, learning-based methods (except DehazeFlow) provide visually better results than other dehazing methods. In the zero-shot dehazing category, our proposed method outperforms other methods in terms of haze removal and color preservation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Methods** & **PSNR** & **SSIM** & **LPIPS** \\ \hline \multirow{3}{*}{Traditional} & _DCP_ & 19.62 & **0.77** & **0.154** \\  & CEEF & **20.26** & 0.75 & 0.163 \\  & FADE & 18.24 & 0.73 & 0.193 \\ \hline \multirow{3}{*}{Learning-based} & DehazeFlow & **19.41** & **0.78** & **0.151** \\  & SRKTDN & 18.26 & 0.74 & 0.186 \\ \cline{1-1}  & DWGAN & 18.89 & 0.77 & 0.163 \\ \cline{1-1}  & RefineD-Net & 18.73 & 0.74 & 0.184 \\ \cline{1-1}  & USIDNet & 18.44 & 0.74 & 0.191 \\ \hline \multirow{3}{*}{Zero-shot} & ZID & 18.16 & 0.74 & 0.189 \\ \cline{1-1}  & DDIP & 19.93 & 0.77 & 0.148 \\ \cline{1-1}  & YOLY & 18.45 & 0.75 & 0.185 \\ \cline{1-1}  & ZIR & **22.04** & 0.86 & 0.1 \\ \cline{1-1}  & Ours & 21.66 & **0.88** & **0.098** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative dehazing evaluation results for RS images with uniform haze. The best value for the specific metric in each category is marked in bold. Figure 4: Comparisons of synthetic RS hazy images with uniform haze. (**a**) Hazy image; (**b**) _DCP_[8]; (**c**) CEEF [6]; (**d**) FADE [41]; (**e**) DehazeFlow [42]; (**f**) SRKTDN [43]; (**g**) DWGAN [44]; (**h**) RefineDNet [17]; (**i**) USIDNet [45]; (**j**) ZID [14]; (**k**) DDIP [35]; (**l**) YOLY [13]; (**m**) ZIR [16]; (**n**) Ours; (**o**) ground truth. Figure 5: Comparisons of images from the synthetic non-uniform RS hazy dataset (SateHaze1k [39]). (**a**) Hazy image; (**b**) _DCP_[8]; (**c**) CEEF [6]; (**d**) FADE [41]; (**e**) DehazeFlow [42]; (**f**) SRKTDN [43]; (**g**) DWGAN [44]; (**h**) RefineDNet [17]; (**i**) USIDNet [45]; (**j**) ZID [14]; (**k**) DDIP [35]; (**l**) YOLY [13]; (**m**) ZIR [16]; (**n**) Ours; (**o**) ground truth. Table 3 displays the results of the quantitative evaluation on the SateHaze1k [39] dataset. We calculated the PSNR, SSIM, and LPIPS metrics for each haze density, and the average value is provided. As shown in Table 3, _DCP_ obtains the best results in the traditional dehazing category, while the performance of different learning-based dehazing methods varies significantly. In the zero-shot dehazing category, our proposed method obtains the best results in all metrics and all haze densities, except for thin haze density, for which it achieved the second-best PSNR value.