# Dense-FG: A Fusion GAN Model by Using Densely Connected Blocks to Fuse Infrared and Visible Images
## 5 Conclusions  
This article proposes a fusion model that uses dense connection blocks to improve the performance of generative adversarial networks in infrared and visible image fusion. Dense connection blocks are introduced in both the generator and discriminator networks to effectively extract complementary information from the source images and enhance  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Expt.** & **SF** & **EN** & **SCD** & **AG** & **SD** & **SSIM** & **PSNR** & \(Q_{F}^{A/B}\) & **VIF** & **MI** \\ \hline A & 8.569 & 6.7146 & 1.5124 & 4.7516 & 30.954 & **0.6897** & 15.460 & 0.4247 & 0.436 & 1.8662 \\ \hline B & 8.728 & 6.6034 & 1.7125 & 4.8224 & 31.597 & 0.6863 & 15.630 & 0.4295 & **0.477** & **1.9733** \\ \hline C & 8.489 & **6.8226** & 1.6903 & 4.6403 & 31.308 & 0.6704 & 14.589 & 0.4106 & 0.461 & 1.8960 \\ \hline \multicolumn{10}{l}{\
\begin{tabular}{c} Dense- \\ FG \\ \end{tabular} } \\ \hline \multicolumn{10}{l}{
\begin{tabular}{c} Bold format represents optimal value for each column. \\ \end{tabular} } \\ \end{tabular}
\end{table}
Table 4: Mean values of image metrics for different fusion algorithms.  
Figure 7: Partial results of ablation experiments. We label the infrared radiation information with a red box and the visible texture information with a blue box for clear comparison.  
the fusion performance. The proposed method was experimentally validated using the TNO dataset.  
In the comparative experiment section, seven other methods and our proposed method, Dense-FG, were used to fuse the same image pairs, and qualitative analysis was performed by calculating the comparative histogram and ten indicators, including SF, EN, SCD, AG, SD, SSIM, \(Q_{F}^{A/B}\), PSNR, VIF, and MI. The results of the comparative histogram and indicators show consistency in their conclusions.  
Traditional methods, including AVG, LP, DWT, and FSDP, exhibit strong performance in structural similarity and image fidelity, but have low image contrast and lack texture details. These methods can be used in special fusion scenarios that require high structural similarity. The FusionGAN method tends to preserve infrared image information, while the DenseFuse method tends to preserve visible information, with high contrast and visual fidelity, but with coarse texture details, and is overwhelmed by details. RFN-Nest performs best in preserving information, but presents average detail with some blurred areas. Overall, our proposed method slightly outperforms the other seven methods and can meet the application requirements of many scenarios.  
The introduction of Dense-FG dense residual blocks enables feature connections between different layers, improves network efficiency, enhances the network's ability to extract source image information, and, compared to other fusion methods, the fused image has rich texture details, high clarity, high retention of source image feature information, and good subjective perception. The introduction of the GAN network's adversarial thinking enables a better balance between the brightness of infrared targets and visible details. Furthermore, the ablation experiment results indicate that the introduction of dense connection blocks in both the generator and discriminator plays a crucial role in improving fusion performance.  
The fusion technology of infrared and visible images is in extensive demand in medical, remote sensing, and other engineering fields. In the future, we will also apply the method proposed in this paper to engineering practice and enable infrared and visible fusion technology to play an important role.  
[PERSON] wrote the draft; [PERSON] gave professional guidance and edited; [PERSON] gave advice and edited. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Natural Science Foundation of China General Program 51979062.  
Not applicable.  
Data aveability Statement: Data openly available in a public repository.  
All authors disclosed no relevant relationships.