## 4 Experiments and Discussion
### Fusion Results on Roadscene Dataset  
To further test the effectiveness of our DAGANFuse, we selected 40 pairs of images from the Roadscene dataset for experimental analysis.  
Firstly, we selected two pairs of typical images, FLIR_07210 and FLIR_06570, for qualitative analysis. The fusion results are shown in Figures 7 and 8. Compared with other methods, DAGANFuse has obvious advantages. Firstly, for the prominent infrared targets, such as the streetlight marked with the red box in Figure 7, DAGANFuse can maintain their original shapes while retaining the original brightness as much as possible. Moreover, for the visible texture information, like the \"STOP SIGN\" and house that are locally magnified with the blue boxes, DAGANFuse preserves clearer information compared to other methods.  
Meanwhile, we conducted a quantitative comparison between DAGANFuse and seven other methods on the Roadscene dataset. The index values are shown in Table 3. Among the six evaluation indicators, DAGANFuse has significant advantages in five of them, namely entropy (EN), average gradient (AG), mutual information (MI), visual information  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **EN** & **SD** & **AG** & **MI** & **VIF** & **SF** \\ \hline FusionGAN & 6.4493 & 27.2618 & 3.0096 & 2.1065 & 0.2846 & 5.9125 \\ DDCGAN & 6.4783 & 41.6072 & 3.9393 & 1.2044 & 0.2432 & 8.1566 \\ MDLatLRR & 6.3292 & 24.0935 & 3.5845 & 1.9596 & 0.3669 & 7.0322 \\ DenseFuse & 6.2892 & 23.2344 & 3.1913 & 2.0259 & 0.3424 & 6.0231 \\ MFEIF & 6.6492 & 31.5080 & 3.4791 & 2.3943 & 0.3842 & 6.7707 \\ Res2 Fusion & 6.9569 & 39.2702 & 4.0239 & 2.7047 & 0.4579 & 8.3721 \\ SDDGAN & 7.0352 & 45.8426 & 3.1325 & 1.3724 & 0.3987 & 6.4354 \\ DAGANFuse & 7.1253 & 41.8938 & 4.0462 & 2.6519 & 0.5367 & 8.5184 \\ \hline \end{tabular}
\end{table}
Table 2: The average metrics values obtained by the existing fusion methods and the proposed network on TNO.  
Figure 8: The fusion results obtained by compared fusion methods and the proposed method on Roadscene (FLIR_06570). These images, in turn, are infrared images, visible images, and the fusion results of FusionGAN, DDCGAN, MDLatLRR, DenseFuse, MFEIF, Res2 Fusion, SDDGAN, and DAGANFuse.  
Figure 7: The fusion results obtained by compared fusion methods and the proposed method on Roadscene (FLIR_07210). These images, in turn, are infrared images, visible images, and the fusion results of FusionGAN, DDCGAN, MDLatLRR, DenseFuse, MFEIF, Res2 Fusion, SDDGAN, and DAGANFuse.  
fidelity (VIF), and spatial frequency (SF). As for the standard deviation (SD) indicator, it ranks fourth, behind DDcGAN, SDDGAN, and MDLatLRR. DAGANFuse achieves the optimal values in three indicators, AG, VIF, and SF, which indicates that the fusion results generated by the proposed model DAGANFuse not only contain richer gradient and texture information but also have better visual effects. The optimal value obtained in the MI indicator shows that DAGANFuse can retain more modality features in the fused image. It also means that the cross-modal differential features attention module proposed in DAGANFuse can effectively distinguish the intrinsic content of different modal images and integrate them effectively into the fusion results. Although DAGANFuse does not achieve the highest value in the SD indicator, from the overall evaluation of the six indicators and the results of qualitative comparison, DAGANFuse has the best fusion effect.