# Patch-Wise Infrared and Visible Image Fusion Using Spatial Adaptive Weights
## 4 Experiments
### Ablation Studies  
In order to illustrate the effect of the gamma on each metric for both type of losses, we perform extensive experiments and the results are summarized in the Figure 4. Gamma indicates different weight for adversarial loss of \(D_{i}\), see Equation (6). We alter gamma as we intend to see the effect of infrared images. For our experiments, we choose \(\gamma\) = 0.1, 0.2, 0.3, 0.5, 0.8, 2, 3 and 5. We can observe that out of the six metrics, four gives highest results for gamma equal to 0.1 and 0.2. However, for each metric except SCD the values are highest between 0 and 1. After 1, the values start to decrease. We show the performance results with two techniques of using the new loss function. We also perform experiments with different values of gamma. We witness that the \(\gamma\) between 0 and 1 yields the best results for each quality metrics expect SCD in Figure 4. We also observe the values for both techniques of using the new loss in \(D_{i}\) only, and in both \(D_{i}\) and \(G\) and see that the loss used in both \(D_{i}\) and \(G\) delivers good performance overall. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **CC** & \(MSSSIM\) & **SCD [33]** & \(FMI_{det}\) [34] & \(SSIM_{a}\) & **PSNR** \\ \hline ours (\(L_{D_{i}}^{*}\), \(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=0.1\) & 1.4715 & 0.9068 & 1.4614 & 0.2684 & 0.7594 & 19.0826 \\ ours (\(L_{D_{i}}^{*}\), \(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=0.5\) & 1.4787 & 0.8933 & 1.3735 & 0.2702 & 0.7615 & 19.17 \\ ours (\(L_{D_{i}}^{*}\), \(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=5\) & 1.3997 & 0.8427 & 0.8844 & 0.2508 & 0.7515 & 19.2911 \\ ours (\(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=0.1\) & 1.4701 & 0.905 & 1.3959 & 0.2714 & 0.7541 & 19.1149 \\ ours (\(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=0.5\) & 1.4702 & 0.8841 & 1.3156 & 0.2527 & 0.7542 & 19.1338 \\ ours (\(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=5\) & 1.4239 & 0.856 & 0.956 & 0.2198 & 0.7543 & 19.2189 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The average values of quality metrics for 20 fused images of our private dataset. Different values of \(\lambda\) and \(\gamma\) are used in the loss function. The best values are indicated in red and the second best values are indicated in blue. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **CC** & \(MSSSIM\) & **SCD [33]** & \(FMI_{det}\) [34] & \(SSIM_{a}\) & **PSNR** \\ \hline U2 Fusion [23] & 1.3785 & 0.8954 & 0.9211 & 0.1953 & 0.7426 & 19.2835 \\ NestFuse [24] & 1.4732 & 0.899 & 1.4616 & 0.2369 & 0.7322 & 18.7873 \\ DDGGAN [12] & 1.2685 & 0.7785 & 1.1741 & 0.1879 & 0.5409 & 11.5284 \\ ours (\(L_{D_{i}}^{*}\), \(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=0.1\) & 1.4715 & 0.9068 & 1.4614 & 0.2684 & 0.7594 & 19.0826 \\ ours (\(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=0.1\) & 1.4701 & 0.905 & 1.3959 & 0.2714 & 0.7541 & 19.1149 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average values of quality metrics for 20 fused images of our private dataset with \(\lambda=0.5\) and \(\gamma=0.1\) used in the loss function. The best values are indicated in red, the second best values are indicated in blue and the third highest values in cyan.