# AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion
## 5 Conclusions  
In this study, we propose a novel adaptive fusion network model for IR and visible images via an adjacent feature fusion approach, called AFF Fusion, which employs a UNet-like network architecture. The model uses a designed AFFF module multiple times in the front section of the network for sequential feature extraction, fusion, and shuffling in the module. This allows the input feature information to fully interact in the feature extraction to enhance the complementarity of feature information. To create fused images that closely approximate human visual perception, the fusion network uses three distinct loss functions: MSE, SSIM, and TV. Additionally, an AWA strategy is used to ensure that the corresponding pixels of IR and visible images have adaptive weighting values that optimize the fusion effect. Finally, by exploiting these improvements, the fusion network outputs high-quality images. Extensive experimental results demonstrate that the proposed AFF Fusion network model produces better-fused images than other state-of-the  
Figure 8: Comparison results of subjective evaluations using the house image from the VOT2020 dataset. (**a**) Visible image. (**b**) IR image. (**c**) GTF. (**d**) MUFusion. (**e**) DCHWT. (**f**) DenseFuse. (**g**) RFN-Nest. (**h**) PMGL. (**i**) SwinFusion. (**j**) U2 Fusion. (**k**) SDNet. (**l**) AFFusion.  
art fusion methods in both subjective and objective metrics, indicating the effectiveness of the designed network architecture.  
In future work, to enhance the overall versatility of the fusion network architecture, we plan to generalize the model to other fusion domains, including multi-exposure and multimodal medical imaging, visible light and millimeter-wave image fusion, as well as visible light and ultrasound image fusion. Additionally, to improve network performance while reducing computational complexity, we will explore the Restormer structure [42], which introduces local receptive fields to limit the scope of the self-attention mechanism in each Transformer layer [43]. Specifically, This approach can reduce computational costs while preserving spatial positional information, allowing the model to handle high-resolution images. Next, it downsamples the spatial dimensions of the input image to varying degrees, allowing the model to simultaneously process multiscale information. Finally, group linear attention mechanisms and short-range connections between different layers can be used to facilitate gradient propagation and reduce the computational complexity of self-attention. With these changes, our AFSFusion will perform better in more metrics while reducing computational complexity.  
Conceptualization, Y.H. and S.X.; methodology, Y.H.; software, X.C.; validation, C.Z., Y.H., S.X. and M.X.; formal analysis, Y.H. and S.X.; investigation, X.C. and M.X.; resources, S.X.; data curation, X.C. and C.Z.; writing--original draft preparation, M.X.; writing--review and editing, Y.H., S.X., X.C. and C.Z.; visualization, Y.H. and S.X.; supervision, Y.H.; project administration, X.C.; funding acquisition, S.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number 62162043.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data will be made available upon request.  
The authors declare no conflict of interest.