# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 4 Experiments and Discussions
### Discussion  
In this section, we discuss the efficacy of the proposed dehazing framework through an ablation study, a convergency test, network size comparison, and a training time complexity analysis. **Ablation study. ** The effectiveness of our proposed loss function is demonstrated through an ablation study, where we remove part of the loss function in Equation (11) and compare the dehazed results. The results of the ablation study are illustrated in Figure 7. As shown, the dehazed result using our proposed loss function (see Figure 7f) is superior to that of removing any part of the loss function. Removing the reconstruction loss causes the entire dehazing framework not to be cycle-inconsistent, resulting in over-enhancement. The TV loss encourages the model to smooth out the details in an image. Therefore, removing the TV loss leads to undesired noise in the sky region in Figure 7c. The _DCP_ loss helps the model remove haze properly, but may result in a dark color. The model without the _DCP_ loss has poor dehazing performance, as shown in Figure 7d. On the other hand, the minimum pixel intensity loss prevents the image pixels from being oversaturated. Thus, removing the minimum pixel intensity loss results in most of the dark details being lost, as shown in Figure 7e. Therefore, the proposed loss function is effective in producing pleasing results for the dehazing framework. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input** & _Precision_ & _Recall_ & _F1-Score_ & _IoU_ \\ \hline Hazy & 0.367 & 0.379 & 0.373 & 0.229 \\ Clear & 0.949 & 0.931 & 0.940 & 0.887 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative comparisons of the D-LinkNet [46] road extraction accuracy using hazy images and clear ground truth images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Method** & _Precision_ & _Recall_ & _F1-Score_ & _IoU_ \\ \hline \multirow{3}{*}{Traditional} & _DCP_ & **0.854** & **0.853** & **0.854** & **0.745** \\  & CEEF & 0.810 & 0.802 & 0.806 & 0.675 \\  & FADE & 0.646 & 0.665 & 0.655 & 0.487 \\ \hline \multirow{3}{*}{Learning-based} & DehazeFlow & 0.652 & 0.652 & 0.652 & 0.484 \\  & SRKTDN & **0.860** & **0.862** & **0.861** & **0.756** \\  & DWGAN & 0.847 & 0.852 & 0.850 & 0.738 \\  & RefineDNet & 0.789 & 0.801 & 0.795 & 0.659 \\  & USIDNet & 0.732 & 0.750 & 0.741 & 0.589 \\ \hline \multirow{3}{*}{Zero-shot} & ZID & 0.614 & 0.636 & 0.625 & 0.455 \\  & DDIP & 0.790 & 0.793 & 0.791 & 0.655 \\ \cline{1-1}  & YOLY & 0.711 & 0.720 & 0.715 & 0.557 \\ \cline{1-1}  & ZIR & 0.819 & 0.823 & 0.821 & 0.697 \\ \cline{1-1}  & Ours & **0.863** & **0.862** & **0.862** & **0.758** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative comparisons of the D-LinkNet [46] road extraction accuracy using the dehazed images and different dehazing methods. The best value for the specific metric in each category is marked in bold. **Convergence test. ** During the training process of a zero-shot dehazing technique, an optimally designed network generates output by minimizing the loss function. However, distinct methods require varying numbers of training steps to produce satisfactory haze-free images, leading to differences in training efficiency. For instance, YOLY [13] necessitates 500 steps, while ZIR [16] demands 10,000 steps to reach the final output. Consequently, we compare the dehazed outcomes produced by various zero-shot approaches during the different training steps to demonstrate the network's convergency. The results are illustrated in Figure 8. At the start of the training process (step 1 in Figure 8a), YOLY's output exhibits significant unwanted flares, which complicates the network convergence for generating superior dehazed results in subsequent training phases. In the case of ZIR, the network produces dehazed outcomes with substantial color shifts until step 100, and the output quality improves at a slow rate, indicating suboptimal convergence efficiency.