# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 6 Conclusions  
In this article, we propose a novel method for lightweight infrared and visible image fusion based on nested connections and Res2 Net. This method combines frequency-domain decomposition, depthwise separable convolution, nested connection networks, and multi-scale residual networks, achieving multi-scale feature extraction while maintaining a small model parameter count. Prior to inputting the source images into the fusion network, a mutually guided filtering operation is applied to better extract hierarchical representations of the images' high- and low-frequency domains. By improving depthwise separable convolution, the model reduces computational complexity while maintaining high fusion quality. Multi-scale feature extraction is realized through the use of nested connection structures. Through the designed R2 FN network, image details are effectively preserved, and significant features of the infrared images are highlighted. Experimental comparisons with several classical image fusion algorithms, in terms of subjective and objective evaluations, demonstrate the superiority of the proposed method across multiple key performance indicators. Notably, the method exhibits advantages in lightweighting, significantly reducing the computational burden, while also enhancing feature extraction and fusion capabilities through the nested connection architecture and the R2 FN fusion module. Consequently, this study not only advances the theoretical and practical aspects of image fusion technology but also opens up new pathways for its application in high-dynamic and dynamic environments.  
Conceptualization, X.T.; methodology, X.T.; software, X.T.; validation, X.T., Y.P., and Q.Y.; formal analysis, Y.P.; investigation, X.T.; resources, X.T.; data curation, X.T.; writing--original draft preparation, X.T.; writing--review and editing, X.T.; visualization, X.T.; supervision, Y.P.; project administration, X.T.; funding acquisition, Y.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Projects of Basic Research Program in Yunnan Province (202401 AS070105), the National Natural Science Foundation of China (61761025), and the Development Fund of Key Laboratory of Computer Technology Application in Yunnan Province (2021102).  
Not applicable.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **EN** & **SD** & **SSIM** & **MI** & **FMI\({}_{det}\)** & **FMI\({}_{w}\)** & **VIF** & **Params** \\ \hline DeepFuse & 6.7581 & 41.8988 & 0.7329 & 13.5161 & 0.3777 & 0.4146 & 0.6582 & 2.9653M \\ DenseFuse & 6.7634 & 41.9003 & 0.7335 & 13.5268 & 0.3810 & 0.4192 & 0.6573 & 3.1317M \\ NestFuse & 6.9355 & 43.5519 & 0.6942 & 13.8709 & 0.3321 & **0.4378** & 0.7249 & 10.9310M \\ FusionGAN & 6.5440 & 38.3732 & 0.6933 & 13.0673 & 0.2831 & 0.3534 & 0.5082 & 7.9873M \\ U2 Fusion & 6.4722 & 30.0504 & 0.7526 & 12.9444 & 0.3077 & 0.3540 & 0.4998 & 5.9896M \\ ITCNN & 6.9521 & 44.5987 & 0.7054 & 13.9041 & 0.3574 & 0.4275 & 0.7279 & 8.4360M \\ Ours & **7.0876** & **45.9123** & **0.7564** & **14.2398** & **0.3975** & 0.4371 & **0.7360** & **2.0432M** \\ \hline \end{tabular}
\end{table}
Table 5: The average quantitative value of each evaluation index. The bold indicates the optimal value.  
Data Availability Statement: The data presented in this study are available upon request from the corresponding author due to confidentiality restrictions.  
The authors declare no conflicts of interest.