# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 4 Experiments and Results
### Fusion Results
#### 4.3.2 Objective Evaluation  
For quantitative comparisons, we chose ten fused images from the results and evaluated them through the above-mentioned eight metrics. The results of the eight metrics are plotted in Figure 10 and the average values are listed in Table 4.  
In Table 4, The values in italic of FusionGAN [26] are not calculated by the authors and they are cited from the original article for reference. Among other values in the table, the best values for each metric are in bold. The proposed method gets the four best values in SF, CC, VIFF, and SCD, also all other metrics are in the top three places, respectively. On the whole, we can further summarize that the proposed fusion method has obvious advantages in getting detailed information from source images and keeping the correlation with source images. Especially, the VIFF index is more salient than others, since the generator is designed with ResNet and skip connections. Also, the loss functions proposed have made appropriate constraints for optimization.  
Firstly, we compare the results with the original FusionGAN [26]. Since we cannot get normal fused results like the images cited in the article from the codes and the trained model provided by the authors, we just use the average values calculated in their article where the source image pairs are mainly consistent with the proposed method. The values may be regarded as a reference, but we cannot make sure whether the source codes of the metrics are the same or not with ours. So we can make an intuitive observation of the images in the article and the proposed method. It is easy to find that the fused image of the proposed method looks more natural and clear, mainly because of the adjustments of the network and loss functions.  
Next, the values of other conventional methods and DL-based methods are compared with the proposed method. From the results shown in Table 4 and Figure 10, we can see that DL-based methods can get commendable achievements in infrared and visible image fusion. The largest SF indicates that the images fused by the proposed method contain more detailed textures. The best value of CC and SCD means that our fused images are highly correlated with the source images. What is more, the proposed method gets the largest value in VIFF which means that visual information is kept as much as possible. This revalidates the conclusion drawn by the subjective evaluation that our fused images look more natural and clearer. The method based on CNN is better than the proposed method  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **EN** & **SF** & **SD** & **MSSIM** & **CC** & **VIFF** & **SCD** & **FMI** \\ \hline CVT & 6.73408 & 11.639 & 29.6285 & 0.5494 & 0.5225 & 0.3693 & 1.5916 & 0.3985 \\ DTCWT & 6.69775 & 11.5691 & 29.2046 & 0.5579 & 0.5265 & 0.3589 & 1.5957 & 0.3876 \\ LP & 6.84323 & 11.8508 & 32.7525 & 0.5665 & 0.5214 & 0.4518 & 1.612 & 0.33449 \\ NSCT & 6.72252 &  
in the metrics of EN and SD, so the fused images always have higher contrast which can easily attract people's attention. The method not only generates the weight maps based on CNN, but also combines the multi-scale transforms, which helped a lot. The method based on Dense-add gets the best values in MSSIM and FMI, probably because the network of Dense-add contains dense blocks that can preserve as much information as possible obtained by each middle layer. The SSIM loss is also used in the Dense-add when training. The proposed method is a little weak in SD and MSSIM, and we will try our best to complement the shortages by optimizing the structure and the loss functions hereafter.  
Taken together, our fusion model can get the best performance among these methods. At the same time, there are no complicated transforms and pre-training needed, which can reduce the computational cost and complexity.  
Figure 10: Quantitative values of eight metrics.