# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 5 Conclusions  
This paper presents a zero-shot dehazing network that combines the strengths of prior-based dehazing and CNN. Initially, the RS hazy image undergoes processing via the _DCP_ based module to obtain the transmission map, atmospheric light, and initial dehazed image. Two shallow UNet-based CNNs are then proposed to refine the transmission map and the initial dehazed image, compensating for the _DCP_'s failure cases. Another hazy image is generated using the atmospheric light, refined transmission map, and the refined dehazed image based on the haze imaging model. The proposed loss function ensures that the network regenerates a hazy image that is similar to the input one, allowing the whole dehazing network to be trained in a zero-shot manner. Overall, the proposed dehazing network has two advantages: (1) the _DCP_ is embedded within the CNN to enhance dehazing performance, mitigate the inherent limitations of _DCP_, and improve overall training efficacy; and (2) the zero-shot learning process improves the network's generalizability without the need for large-scale training data collections. In the experiments, real-world and synthetic RS hazy images were selected to verify the effectiveness of the proposed method. The qualitative and quantitative evaluation results indicate that our method  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **ZID [14]** & **DDP [35]** & **YOLY [13]** & **ZIR [16]** & **Ours** \\ \hline Params size (MB) & 39.49 & 1.64 & 38.14 & 0.51 & **0.12** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Network size comparison of different zero-shot dehazing methods. The best result is marked in bold.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image Size** & **ZID [14]** & **DDIP [35]** & **YOLY [13]** & **ZIR [16]** & **Ours** \\ \hline \(512\times 512\) & 19.38 & 74.43 & 19.59 & 311.95 & **6.35** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training time (seconds) comparison of different zero-shot dehazing methods. The best result is marked in bold.  
Figure 8: Comparisons of images from the dehazed results using different zero-shot methods during the training process, where the first row to third row are the results via ZIR, YOLY, and Ours. (**a**)–(**e**) are the dehazed results of training steps 1, 100, 200, 300, and 500, respectively, all of which were different.  
outperforms many other dehazing methods in processing both uniform and non-uniform RS hazy images. Furthermore, the proposed method's superiority is demonstrated in a high-level vision task (road extraction).  
Conceptualization, J.W. and Y.W.; methodology, J.W. and K.Y.; software, J.W. and Y.C.; validation, Y.W. and K.Y.; data curation, J.W. and Y.C.; funding acquisition, J.W. and Y.W.; writing--original draft preparation, J.W.; writing--review and editing, J.W., Y.C., K.Y., Y.W. and L.C.; supervision, Y.W., L.C. and K.Y. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (U1805262, 61901117, 62201152); the special Funds of the Central Government Guiding Local Science and Technology Development (2021L3010); Key provincial scientific and technological innovation projects (2021G02006); the Natural Science Foundation of Fujian Province, China (2022J01169, 2020J01157); the Scientific Research Project of Fujian Jiangxia University (JXZ021013); and the Education and Scientific Research Project for Middle-aged and Young Teachers in Fujian Province (JAT200370).  
The data that support the findings of this study are available on request from the corresponding author.  
All authors declare no conflict of interest.