# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 5 Experiments and Results Analysis
### Ablation Study
#### 5.3.1 Frequency-Domain Decomposition  
In this section, an analysis is conducted on the frequency-domain decomposition module, and the influence of different parameters of \(\alpha\) in the guided filtering operation (muGIF) on the network is examined.  
As discussed in Section 3.1, mutually guided image filtering is employed during frequency-domain decomposition to decompose the input source image into base and detail layers. The quality of filtering critically impacts the final image fusion performance. Therefore, in the experiments of this section, \(\alpha\) is set to 0.0001, 0.001, 0.01, 0.002, 0.003, and 0.004 to analyze its influence on the filtering effect. (Only the experimental results of infrared image frequency-domain decomposition are listed in this paper, and it is observed that the trends in the decomposition effects of visible-light images are consistent with those of infrared images.) The experimental results are shown in Figures 8 and 9.  
From the figure, it can be observed that, with the increase in \(\alpha\), the base layer gradually becomes smoother, as larger \(\alpha\) values result in the removal of more high-frequency details. When \(\alpha\) increases to a certain extent, such as \(\alpha=0.01\), the detail layer loses too much high-frequency information, leading to less prominent image details. At \(\alpha=0.003\), the base layer demonstrates moderate smoothness, removing an appropriate amount of high-frequency detail information while still retaining sufficient structural information. The detail layer preserves more texture and edge information without excessive smoothing, indicating that the filter can better distinguish between the base content and detail content. Therefore, we select \(0.003\) as the value of \(\alpha\).  
After determining the value of \(\alpha\), to verify the impact of the frequency-domain decomposition module on the performance of image fusion, we conducted ablation experiments targeting this module using the same image fusion network. The experimental results are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **EN** & **SD** & **SSIM** & **MI** & **FMI\({}_{\mathbf{dct}}\)** & **FMI\({}_{\mathbf{w}}\)** & **VIF** \\ \hline Exp.1 & 6.7597 & 42.4632 & 0.7348 & 14.0251 & 0.3783 & 0.4132 & 0.6531 \\ Exp.2 & 7.0876 & 45.9123 & 0.7564 & 14.2398 & 0.3975 & 0.4371 & 0.7360 \\ \hline \end{tabular}
\end{table}
Table 1: The average values of objective metrics obtained without using the frequency-domain decomposition module and with the frequency-domain decomposition module included.  
Figure 8: The infrared base layer images corresponding to different \(\alpha\) values of mutually guided filtering.  
Figure 9: The infrared detail layer images corresponding to different \(\alpha\) values of mutually guided filtering.  
In the table, Experiment 1 corresponds to the fusion results without the frequency-domain decomposition module, while Experiment 2 corresponds to the fusion results with the inclusion of the frequency-domain decomposition module. Clearly, after incorporating the frequency-domain decomposition module, the numerical values of various evaluation metrics improved, validating the effectiveness of this module in enhancing the performance of the image fusion network.