# Patch-Wise Infrared and Visible Image Fusion Using Spatial Adaptive Weights
## 4 Experiments
### Quantitative Analysis  
For the quantitative comparison, we take the average value of 20 fused images for each metric. In this comparison, we analyze the new weighted loss in discriminator \(D_{ir}\) and generator G, and also in discriminator \(D_{ir}\) only. In Tables 1 and 2, we have taken fixed values of \(\lambda=0.5\) and \(\gamma=0.1\). Table 1 displays the values of different metrics for RoadScene dataset while Table 2 displays the values for our private dataset. The best values are indicated in red, the second best values are indicated in blue, and the third highest values in cyan.  
From Table 1, we can see that as compared to the state-of the-art methods, our method achieves the top-2 results in CC and \(SSIM_{a}\). The second and third best results in MS-SSIM, SCD and PSNR. The second best result has a narrow margin of only 0.018 from the best result in PSNR, a difference of only 0.0252 from the best result in MS-SSIM and a difference of 0.0386 from the best result in SCD.  
In Table 2, as compared to the other state-of the-art methods, our method acquires the top-2 ranks in MS-SSIM, \(FMI_{dct}\) which calculates the mutual information for discrete cosine features and \(SSIM_{a}\). The second best and the third best results in CC, SCD and PSNR. CC achieves the second best result with a difference of 0.0017 from the best result. SCD has a difference of only 0.0002 and PSNR has a margin of 0.16 from the best results. The highest values in \(FMI_{dct}\) and \(SSIM_{a}\) indicate that our method attains more features and structural information. Highest value in PSNR indicates that the fused image is more similar to the source images and is of higher quality with less distortion. In general, our method gives better performance than DDcGAN by simply replacing the GAN with PatchGAN and adding weights. These results prove the effectiveness of our method.  
We also compare the different values of \(\gamma\) in Tables 3 and 4. Here, the best values are indicated in red and the second best values are indicated in blue. From Tables 3 and 4, we can witness that most of the highest results are achieved with \(\lambda=0.5\) and \(\gamma=0.1\).  
Figure 2: Qualitative comparison of the proposed method with other state-of-the-art methods on image pairs taken from the private dataset. The first row contains visible images. Second row contains infrared images. The last row contains the fused results of our proposed method.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Methods** & **CC** & \(MSSSIM\) & **SCD**[33] & \(FMI_{Lat}\) [34] & \(SSIM_{a}\) & **PSNR** \\ \hline U2 Fusion [23] & & 1.2199 & 0.8907 & 1.3236 & 0.3057 & 0.7204 & 16.0903 \\ NestFuse [24] & & 1.1889 & 0.8376 & 1.6574 & 0.2969 & 0.6598 & 13.8161 \\ DDGGAN [12] & & 1.1752 & 0.7067 & 1.5041 & 0.3589 & 0.5965 & 13.8019 \\ ours (\(L_{D_{i}}^{L_{D_{i}}^{obs}}\)) & \(\lambda=0.5\), \(\gamma=0.1\) & 1.2675 & 0.8655 & 1.6188 & 0.2691 & 0.7381 & 16.0222 \\ ours (\(L_{D_{i}}^{obs}\)) & \(\lambda=0.5\), \(\gamma=0.1\) & 1.2587 & 0.8625 & 1.5609 & 0.2736 & 0.7396 & 16.0723 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average values of quality metrics for 20 fused images of our RoadScene dataset with \(\lambda=0.5\) and \(\gamma=0.1\) used in the loss function. The best values are indicated in red, the second best values are indicated in blue and the third highest values in cyan.  
Figure 3: Qualitative comparison of the proposed method with other state-of-the-art methods on image pairs taken from the RoadScene dataset. The first row contains visible images. Second row contains infrared images. The last row contains the fused results of our proposed method.