# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
## 4 Experimental Setting and Results Analysis
### Subjective Comparisons  
Figure 3 shows the results obtained by ten fusion methods on images from the TNO dataset. In terms of preservation of the infrared brightness information, the six methods based on VggML, Resnet50, BayF, AUIF, CSF, and YDTR yield relatively dim infrared targets, e.g., the persons and wheels in multiple scenes. The fusion results of the DDoGAN, SeAFusion, and VEHMD methods manifest that these can compensate for the abovementioned shortcomings; however, there are some problems that must be noted: the DDoGAN over enhances the contrast of the source images, resulting in the sharpening of both the infrared targets and texture details, and the visible details of SeAFusion are partially lost, e.g., the branches of the trees are intertwined rather than separated, as can be observed from the magnified blue regions. The VEHMD generates undesirable noise and artifacts that alter the image characteristics. In contrast, the proposed method not only maintains the infrared target brightness well but also finely preserves the visible details without noticeable artifacts. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Metrics**} & \(\mathbb{Q}_{\text{abf}}\) & \(\mathbb{SSIM}\) & \(\mathbb{Q}_{\text{m}}\) & \(\mathbb{Q}_{\text{p}}\) & \(\mathbb{FMI}_{\text{dct}}\) & \(\mathbb{FMI}_{\text{w}}\) \\ \hline \(\omega_{1}=1.0\) & \(\omega_{2}=1.0\) & 0.4897 & 0.8336 & 0.7080 & 0.3931 & 0.8922 & 0.4133 \\ \(\omega_{1}=1.0\) & \(\omega_{2}=1.3\) & 0.4908 & 0.8347 & 0.7090 & 0.3958 & 0.8923 & 0.4140 \\ \(\omega_{1}=1.0\) & \(\omega_{2}=1.5\) & 0.4912 & 0.8351 & **0.7096** & 0.3969 & 0.8925 & 0.4144 \\ \(\omega_{1}=1.0\) & \(\omega_{2}=1.8\) & 0.4916 & 0.8357 & 0.7095 & 0.3984 & **0.8926** & 0.4149 \\ \(\omega_{1}=1.0\) & \(\omega_{2}=2.0\) & 0.4906 & 0.8326 & 0.7059 & 0.3886 & 0.8885 & 0.4134 \\ \(\omega_{1}=1.0\) & \(\omega_{2}=2.3\) & 0.4919 & 0.8362 & 0.7093 & 0.3994 & **0.8926** & 0.4154 \\ \(\omega_{1}=1.0\) & \(\omega_{2}=2.5\) & 0.4920 & 0.8363 & 0.7085 & 0.3998 & **0.8926** & 0.4155 \\ \(\omega_{1}=1.0\) & \(\omega_{2}=2.8\) & **0.4921** & **0.8365** & 0.7086 & **0.4005** & **0.8926** & **0.4158** \\ \(\omega_{1}=1.0\) & \(\omega_{2}=3.0\) & 0.4920 & **0.8365** & 0.7088 & **0.4005** & **0.8926** & **0.4158** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Averages of the six metrics for different \(\omega_{2}\) on ten pairs of source images from two public datasets. Numbers in bold font represent the best value. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & \(\sigma_{r}=3*3\) & \(\sigma_{r}=5*5\) & \(\sigma_{r}=7*7\) & \(\sigma_{r}=9*9\) \\ \hline \(\mathbb{Q}_{\text{abf}}\) & **0.4947** & 0.4879 & 0.4835 & 0.4806 \\ SSIM & **0.8381** & 0.8338 & 0.8310 & 0.8287 \\ \(\mathbb{Q}_{\text{m}}\) & **0.7106** & 0.7025 & 0.6947 & 0.6900 \\ \(\mathbb{Q}_{\text{p}}\) & **0.4011** & 0.3987 & 0.3967 & 0.3951 \\ \(\mathbb{FMI}_{\text{dct}}\) & **0.8927** & 0.8921 & 0.8921 & 0.8920 \\ \(\mathbb{FMI}_{\text{w}}\) & **0.4160** & 0.4152 & 0.4145 & 0.4138 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Averages of the six metrics for different \(\sigma_{r}\) on ten pairs of source images from two public datasets. Numbers in bold font represent the best value. Figure 3: Subjective result images of the ten fusion methods. From top to bottom in order: infrared images (IR), visible images (VIS), VggML, Resnet50, BayF, AUIF, CSF, DDCGAN, SeAFusion, VEHMD, YDTR, and our method (Ours). From left to right in order: “Camp”, “Octec”, “Kaptein 1654”, “Jeep”, “Sand-path”, and “Kaptein 1123”. The red and blue boxes show the enlarged local regions. For further comparison, two groups of image pairs from the RoadScene dataset are examined to obtain the fused results, as shown in Figures 4 and 5. In Figure 4, the overall contrast based on the VggML in Figure 4c, Resnet50 in Figure 4d, BayF in Figure 4e, and VEHMD in Figure 4j are dim, making it difficult to distinguish between infrared brightness information and texture details.