# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
## 4 Experimental Setting and Results Analysis
### Parameter Discussion  
There are several parameters that need to be discussed to find the optimal values, as shown in Table 1. In this subsection, eight sets of images from the TNO dataset and two groups of images from the RoadScene dataset are averaged for the six objective indicators, and the average value is the largest, and the optimal value is more significant. The role of FGF is to eliminate possible discontinuities and noise in the combined maps. As shown in (13), to determine the optimal parameters, we follow the variable transformation rule, namely, varying one parameter while the others remain fixed. Table 2 shows the average values of the six indicators for different \(\varepsilon\), and it is seen that when \(\varepsilon\) is 0.1, the averages are the largest. Similarly, we can conclude that the maximum value occurs when \(r\) is 8 and \(s\) is 2 from the results in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**FGF**} & \multicolumn{2}{c}{\(S_{n}\)} & \(R_{n}\) \\ \cline{2-7}  & \(r\) & \(\varepsilon\) & \(s\) & \(\omega_{1}\) & \(\omega_{2}\) & \(\sigma_{r}\) \\ \hline Optimal value & 8 & 0.1 & 2 & 1.0 & 2.8 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters in the proposed method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \(\varepsilon=10^{-1}\) & \(\varepsilon=10^{-2}\) & \(\varepsilon=10^{-3}\) & \(\varepsilon=10^{-4}\) \\ \hline Q\({}_{\text{ab}t}\) & **0.4894** & 0.4816 & 0.4456 & 0.4091 \\ SSIM & **0.8349** & 0.8270 & 0.7854 & 0.7428 \\ Q\({}_{\text{m}}\) & **0.7102** & 0.6878 & 0.6246 & 0.5845 \\ Q\({}_{\text{P}}\) & **0.3974** & 0.3866 & 0.3361 & 0.2891 \\ FMI\({}_{\text{dct}}\) & **0.8924** & 0.8916 & 0.8870 & 0.8821 \\ FMI\({}_{\text{w}}\) & **0.4146** & 0.4098 & 0.3887 & 0.3713 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Averages of the six metrics for different \(\varepsilon\) 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 c} \hline \hline \multirow{2}{*}{**Metrics**} & \(r=2\) & \(r=2\) & \(r=4\) & \(r=4\) & \(r=8\) & \(r=8\) \\  & \(s=0.5\) & \(s=2\) & \(s=1\) & \(s=4\) & \(s=2\) & \(s=8\) \\ \hline Q\({}_{\text{ab}t}\) & 0.4757 & 0.4710 & 0.4844 & 0.4766 & **0.4920** & 0.4774 \\ SSIM & 0.8270 & 0.8237 & 0.8323 & 0.8269 & **0.8363** & 0.8266 \\ Q\({}_{\text{m}}\) & 0.6922 & 0.6837 & 0.7049 & 0.6917 & **0.7092** & 0.6858 \\ Q\({}_{\text{p}}\) & 0.3877 & 0.3851 & 0.3939 & 0.3897 & **0.3994** & 0.3909 \\ FMI\({}_{\text{dct}}\) & 0.8922 & 0.8919 & 0.8924 & 0.8910 & **0.8927** & 0.8909 \\ FMI\({}_{\text{w}}\) & 0.4096 & 0.4085 & 0.4127 & 0.4115 & **0.4154** & 0.4119 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Averages of the six metrics for different \(r\) and \(s\) values on ten pairs of source images from two public datasets. Numbers in bold font represent the best value. As for the parameters \(\omega_{1}\) and \(\omega_{2}\), we know from the above discussion that these determine the importance of the gradient intensity and exposedness strength in the fused image, and the more important features are assigned higher weight ratios. In the IBSM, the focus is more on extracting the infrared heat information missing from the results of the SGCM from the source images and injecting them into the fused images. In other words, we need to ensure that the gradient strength in the fused image remains unchanged when injecting the infrared heat information to realize effective complementation of the two modules. We thus set \(\omega_{1}\) to 1.0, and \(\omega_{2}\) is assigned a higher weight. Through extensive experiments on eight sets of source images and evaluation of the six indicators, the change trend of \(\omega_{2}\) is seen in Table 4 while \(\omega_{1}\) defaults to 1.0. From the table, we find that the average of five of the indicators is at maximum when \(\omega_{2}\) is 2.8, except for \(\mathbb{Q}_{\text{m}}\).