# AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion
## 4 Experimental Results and Analysis
### Analysis of Results
#### 4.4.2 Subjective Evaluation  
To visually evaluate the fusion results of the fused images obtained for each method, a subjective comparison of artificial vision was performed, and the results are shown in Figures 7 and 8. The results presented in this study are based on the region defined by the red rectangular box in Figure 7. The target contours in the images fused using the GTF, PMGI, RFN-Nest, and SwinFusion methods appear blurred. While the DCHWT, DenseFuse, and U2 Fusion methods avoid this problem, the fused images are not clear. Results are further analyzed in the region defined by the yellow box in Figure 7, where most compared methods are found to be strongly influenced by the IR image, resulting in dark fusion outcomes for the wheel part. Both U2 Fusion and SDNet results are strongly influenced by visible light, resulting in the target and building appearing white, which does not match the visual perception characteristics of the human eye. The AFSFusion model produces the best visual results. A comprehensive comparison with state-of-the-art algorithms reveals that AFSFusion has better fusion effects, with fused image targets being clearer and more consistent with human visual characteristics. As shown in Figure 8, results are further observed in the area defined by the blue box of each fusion result. The GTF, RFN-Nest, and SDNet methods show a significant loss of contour information, while several methods, such as DenseFuse, SwinFusion, PMGI, U2 Fusion, and MUFusion, achieve better fusion results but still suffer from IR image whitening. Additionally, the DCHWT fusion results contain many artifacts that severely degrade the visual effect. In conclusion, the AFSFusion network model outperforms state-of-the-art methods by producing fused images with clearer target contours and higher levels of texture detail. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **SwinFusion** & **DenseFuse** & **PMGI** & **U2 Fusion** & **MUFusion** & **SDNet** & **GTF** & **AFSFusion** \\ \hline En & 6.83 & 6.38 & 6.96 & 6.96 & 6.68 & 6.71 & 6.77 & **7.11** \\ SD & 89.19 & 55.32 & 76.69 & 76.78 & 65.47 & 70.79 & 83.04 & **95.08** \\ VIF & **0.92** & 0.68 & 0.85 & 0.80 & 0.72 & 0.75 & 0.62 & 0.89 \\ MI & 13.66 & 12.75 & 13.92 & 13.91 & 13.37 & 13.42 & 13.53 & **14.22** \\ MS-SSIM & 0.90 & 0.88 & 0.89 & **0.93** & 0.88 & 0.88 & 0.82 & 0.89 \\ NIQMC & 4.51 & 3.75 & 4.58 & 4.55 & 4.18 & 4.23 & 4.37 & **4.80** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the fusion results for each fusion method using the VOT2020 dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & **SwinFusion** & **DenseFuse** & **PMGI** & **U2 Fusion** & **MUFusion** & **SDNet** & **GTF** & **AFSFusion** \\ \hline En & 6.83 & 6.38 & 6.97 & 6.90 & 6.71 & 6.57 & 6.72 & **7.16** \\ SD & 93.03 & 59.79 & 77.70 & 76.91 & 71.13 & 62.96 & 76.74 & **95.95** \\ VIF & **0.90** & 0.69 & 0.84 & 0.79 & 0.73 & 0.73 & 0.63 & 0.88 \\ MI & 13.66 & 12.76 & 13.94 & 13.79 & 13.43 & 13.16 & 13.44 & **14.32** \\ MS-SSIM & 0.90 & 0.88 & 0.88 & **0.92** & 0.90 & 0.86 & 0.82 & 0.91 \\ NIQMC & 4.52 & 3.77 & 4.59 & 4.48 & 4.23 & 4.07 & 4.29 & **4.84** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of fusion results for each fusion method using the TNO dataset. Figure 5: Comparison of fusion results of each fusion method for the TNO dataset. The following are acronyms for different method models: SwinFusion (Sw), DenseFuse (De), PMGI (PM), U2 Fusion (U2), MUFusion (MU), SDNet (SD), GTF (GTF), and AFFusion (AF). (**a**) En. (**b**) SD. (**c**) MI. (**d**) MS-SSIM. (**e**) VIF. (**f**) NIQMC. Figure 6: Comparison of the fusion results for each fusion method using the VOT2020 dataset. The following are acronyms for different method models: SwinFusion (Sw), DenseFuse (De), PMGI (PM), U2 Fusion (U2), MUFusion (MU), SDNet (SD), GTF (GTF), and ASFFusion (AF). (**a**) En. (**b**) SD. (**c**) MI. (**d**) MS-SSIM. (**e**) VIF. (**f**) NIQMC.