# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 5 Experiments and Results Analysis
### Results Analysis and Comparison  
#### 5.4.1 Subjective Evaluation  
To validate the effectiveness of our proposed method, we conducted a subjective comparative experiment on a subset of images from the TNO dataset, comparing them with various image fusion algorithms. The comparative results of each algorithm are shown in Figure 10. The comparison results show that the FusionGAN method fails to effectively preserve detailed texture information from the visible-light images. NestFuse and IFCNN methods demonstrate a good representation of the target contours but do not effectively retain the thermal radiation information from the infrared images. The DeepFuse, DenseFuse, and U2 Fusion methods exhibit clear contour information and target features, but the introduction of excessive noise leads to poor fused image quality. Particularly in the yellow-boxed areas in Figure 10, none of these algorithms achieve the desired fusion results. In contrast, our algorithm maintains a better balance of information between the infrared and visible-light images in most fusion scenarios, resulting in superior fusion results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\beta\) & **EN** & **SD** & **SSIM** & **MI** & \(\text{FMI}_{det}\) & \(\text{FMI}_{w}\) & **VIF** \\ \hline
100 & 6.6453 & 43.7460 & 0.7438 & 13.9182 & 0.3405 & 0.4230 & 0.7141 \\
300 & 6.7841 & 44.6525 & 0.7532 & 14.0203 & 0.3544 & 0.4366 & 0.7324 \\
500 & **7.0876** & **45.9123** & **0.7564** & **14.2398** & **0.3975** & 0.4371 & **0.7360** \\
700 & 6.8911 & 43.9084 & 0.7561 & 14.2369 & 0.3607 & **0.4505** & 0.7240 \\
1000 & 6.7140 & 43.5906 & 0.7505 & 14.1427 & 0.3460 & 0.4312 & 0.7244 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average values of objective metrics obtained were calculated by setting \(\omega_{\text{ir}}\) to 5 and \(\omega_{\text{vi}}\) to 3 during the second stage of training while varying the values of \(\beta\). The bold indicates the optimal value.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\omega_{\text{ir}}\) & \(\omega_{\text{vi}}\) & **EN** & **SD** & **SSIM** & **MI** & \(\text{FMI}_{det}\) & \(\text{FMI}_{w}\) & **VIF** \\ \hline
2 & 2 & 6.8374 & 43.3473 & 0.7117 & 13.8329 & 0.3211 & 0.4121 & 0.6826 \\ \hline
3 & 2 & 6.8479 & 43.5035 & 0.7322 & 13.8838 & 0.3415 & 0.4334 & 0.7038 \\  & 3 & 6.8558 & 43.6115 & 0.7380 & 13.9025 & 0.3448 & 0.4366 & 0.7086 \\ \hline
4 & 2 & 6.8397 & 43.3764 & 0.7172 & 13.8530 & 0.3264 & 0.4178 & 0.6886 \\  & 3 & 6.8544 & 43.5587 & 0.7377 & 13.8955 & 0.3475 & 0.4389 & 0.7092 \\  & 4 & 6.8624 & 43.6606 & 0.7412 & 13.9225 & 0.3505 & 0.4419 & 0.7131 \\ \hline
5 & 2 & 6.8655 & 43.7051 & 0.7230 & 13.8641 & 0.3324 & 0.4240 & 0.6945 \\  & 3 & **6.8911** & 43.9084 & **0.7561** & **14.2369** & **0.3607** & **0.4505** & 0.7240 \\  & 4 & 6.8833 & 44.1212 & 0.7473 & 13.9342 & 0.3557 & 0.4475 & 0.7174 \\  & 5 & 6.8694 & 43.7629 & 0.7278 & 13.8762 & 0.3382 & 0.4298 & 0.7003 \\ \hline  & 2 & 6.8803 & 43.9310 & 0.7396 & 13.9350 & 0.3489 & 0.4408 & 0.7117 \\  & 3 & 6.8905 & **44.1225** & 0.7515 & 13.9753 & 0.3543 & 0.4461 & 0.6980 \\
6 & 4 & 6.8730 & 43.8484 & 0.7326 & 13.8878 & 0.3362 & 0.4274 & **0.7272** \\  & 5 & 6.8612 & 43.6517 & 0.7197 & 13.8518 & 0.3240 & 0.4152 & 0.6860 \\  & 6 & 6.8494 & 43.4549 & 0.7066 & 13.8164 & 0.3116 & 0.4030 & 0.6741 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average values of objective metrics obtained were calculated by setting \(\beta\) to 700 during the second stage of training and by varying the values of \(\omega_{\text{ir}}\) and \(\omega_{\text{vi}}\). The bold indicates the optimal value.