## 4 Experiment
### Comparative Experiments
#### 4.3.1 Land Scene  
In the land scene, the OTS is used for training models and comparison methods. SOTS-outdoor was synthesized in the same way as OTS and was selected as one of the test sets. SOTS-indoor and I-HAZE differ in terms of scenes and haze types, and the results from these two test sets can reflect the adaptability of the model to different scenes. The comparison results are shown in Table 1. The visualization results are shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Metrics**} & \multicolumn{2}{c}{**SOTS-Outdoor**} & \multicolumn{2}{c}{**SOTS-Indoor**} & \multicolumn{2}{c}{**I-HAZE**} \\ \cline{2-9}  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline \multirow{4}{*}{**Methods**} & (a) FFANet & **36.361** & **0.993** & 20.230 & 0.855 & 16.437 & 0.747 \\  & (b) C2 PNet & 36.180 & 0.989 & 21.213 & 0.897 & 16.650 & 0.733 \\  & (c) MSBDN & 33.794 & 0.983 & 21.330 & 0.837 & 16.056 & 0.769 \\ \hline \multirow{4}{*}{**Methods**} & (d) DCP & 18.158 & 0.804 & 18.640 & 0.792 & 14.444 & 0.629 \\  & (e) Cycle-Dehaze & 15.835 & 0.789 & 11.961 & 0.688 & 14.318 & 0.605 \\ \cline{1-1}  & (f) Ours & 26.248 & 0.945 & **22.385** & **0.903** & **16.681** & **0.816** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison results of different models using the SOTS-outdoor, SOTS-indoor, and I-HAZE datasets. In the experimental comparisons using the SOTS-outdoor dataset as the test set, strong fitting capabilities were demonstrated in the supervised FFANet due to the consistency between SOTS-outdoor and the training set OTS in synthetic methods. The best results were achieved using our method among those trained on non-paired datasets. Under unsupervised conditions, image features can be learned effectively by the method, leading to high-quality predictions and demonstrating its potential and advantages in the absence of paired data. In the experimental comparisons using the SOTS-indoor and I-HAZE datasets as the test sets, due to the inconsistency between the test sets and OTS in synthetic methods, the supervised model failed to adapt effectively to changes in the data distribution. In this case, the advantages of supervised methods are significantly diminished, and there is even a slight overfitting phenomenon, leading to a decline in performance on both datasets. In contrast, our proposed method exhibits stable performance on both datasets. In the two image quality evaluation indices of SSIM and PSNR, compared with other existing comparison methods, the proposed method shows better adaptability across diverse datasets. Based on the experimental results, it can be observed that the proposed method not only overcomes the adverse effects caused by the inconsistency of synthetic datasets but also maintains relatively consistent performance across different test sets. This further verifies its effectiveness and feasibility within unsupervised learning. As can be seen in the visualization results in Figure 8d, DCP is a classic prior-based method that relies on the dark channel prior derived from the atmospheric scattering model. However, it struggles to adapt to complex scenes and often leads to color distortions or over-dehazing. C2 PNet, shown in Figure 8b, explicitly incorporates the atmospheric scattering model by proposing a physical-aware dual-branch unit, which assigns the estimation of transmittance and atmospheric light to two separate learning branches. This design significantly improves the accuracy of key parameter estimation, thus enhancing the model's robustness. On the other hand, cycle consistency constraints for unsupervised learning is introduced by CycleDehaze, but explicit modeling of the physical scattering process is lacking, resulting in weaker performance in physical consistency and detail restoration, as shown in Figure 8e. A feature fusion attention module is introduced by FFANet to adaptively weight features at different scales and regions, as shown in Figure 8a.