## 5 Conclusions  
In this paper, we propose a multi-scene image dehazing method, FA-CycleGAN. We use the improved CycleGAN for image dehazing while establishing a physical model for the dehazing process. The atmospheric scattering model is integrated to physically constrain the dehazing process, which not only uses the prior knowledge of the physical model but also corrects inherent estimation errors through the deep learning model. A feature fusion attention module is introduced in the generator network. This module performs multi-scale feature fusion and feature weighting on features of different levels to better learn features of varying distributions and improve the adaptability of the model to different scenes. Extensive experiments on multiple datasets demonstrate the superior performance of FA-CycleGAN, confirming its robustness in image dehazing tasks. This method has shown certain effectiveness across different datasets, effectively improving model performance. Dehazed images can retain more details and exhibit better visual effects. However, there is still room for improvement. Due to time and equipment limitations, the scale and diversity of the established datasets often do not fully reflect the complexity and variability of real environments. Therefore, future research needs to focus on the establishment of complex and variable environmental datasets that include multiple weather conditions and illumination levels, so that the model can better adapt to real complex environments. Figure 13: (**a–e**) Visualization results of five different haze densities before and after dehazing the MDD dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\beta\)} & \multirow{2}{*}{**A**} & \multicolumn{4}{c}{**PSNR**} & \multicolumn{4}{c}{**SSIM**} \\ \cline{2-9}  & & **0.8** & **0.9** & **0.95** & **Random** & **0.8** & **0.9** & **0.95** & **Random** \\ \hline
0.04 & 23.578 & 24.850 & 22.443 & 23.716 & 0.951 & 0.956 & 0.958 & 0.953 \\
0.08 & 20.817 & 22.430 & 12.424 & 21.233 & 0.935 & 0.942 & 0.817 & 0.939 \\ Random & 22.563 & 23.187 & 20.196 & 22.468 & 0.941 & 0.938 & 0.905 & 0.941 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Objective comparison results using different haze densities. **Author Contributions:** Conceptualization, N.L. ([PERSON]); methodology, N.L. ([PERSON]) and Y.D. ; software, N.L. ([PERSON]); validation, N.L. ([PERSON]); formal analysis, Y.C. ; data curation, N.L. ([PERSON]); writing--original draft preparation, N.L. ([PERSON]) and N.L. ([PERSON]); writing--review and editing, Y.D. and Y.C. ; supervision, N.L. ([PERSON]); funding acquisition, N.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 62002285) and, in part, by the Youth Innovation Team of Shaanxi Universities. **Institutional Review Board Statement:** Not applicable for studies not involving humans or animals. **Data Availability Statement:** The original data (RESIDE) presented in this study are openly available at [[https://sites.google.com/view/reside-dehaze-datasets](https://sites.google.com/view/reside-dehaze-datasets)]([https://sites.google.com/view/reside-dehaze-datasets](https://sites.google.com/view/reside-dehaze-datasets)) (accessed on 25 January 2024). The original data (I-HAZE) presented in this study are openly available at [[https://data.vision.ee.ethz.ch/cvl/ntire18//i-haze/](https://data.vision.ee.ethz.ch/cvl/ntire18//i-haze/)]([https://data.vision.ee.ethz.ch/cvl/ntire18//i-haze/](https://data.vision.ee.ethz.ch/cvl/ntire18//i-haze/)) (accessed on 12 February 2024). The data in the MDD presented in this study are available on the Kaggle Datasets website at [[https://www.kaggle.com/](https://www.kaggle.com/)]([https://www.kaggle.com/](https://www.kaggle.com/)) (accessed on 17 March 2024).