# Dense-FG: A Fusion GAN Model by Using Densely Connected Blocks to Fuse Infrared and Visible Images
## 4 Experiments and Results
### Comparative Experiment
The pixel distribution of the RFN-Nest is relatively continuous, indicating that the image is delicate and uniform, but with blurred edges. The Dense-FG histogram has a layered distribution, but the frequency values of prominent pixels are low, indicating that the image details are more prominent. At the same time, it takes the delicacy of the image into account, and the overall visual impression is good. The width of the histogram can clearly reflect the contrast of the image. Among the four deep learning fusion methods, DenseFuse has the highest width and the greatest contrast, followed by RFN-Nest, Dense-FG, and FG, in that order. The average values of the fusion images generated by different algorithms under various evaluation metrics are shown in Table 3. The metrics represent the richness of  
Figure 5: Comparison of histograms of fused results from different algorithms. texture details, information entropy, complementary information, clarity, contrast, similarity with the source image structure, richness of edge information, image fidelity, preservation of source image information, and visual information fidelity of the fusion image. The metrics are positively correlated, with larger values indicating higher quality fusion images. Table 3 presents the comparative results of eight methods under 10 metrics, with the optimal values highlighted in red bold and the second optimal values highlighted in blue bold. From Table 3, it can be seen that for the SF metric, the proposed method outperforms DenseFuse by about 15.9%, FusionGAN by about 71.06%, and LP by about 2.65%, with the fusion image containing rich texture details. For the AG metric, the proposed method outperforms DenseFuse by about 16.98%, FusionGAN by about 75.96%, and RFN-Nest by about 69.81%, with high image clarity. For the SD metric, the proposed method slightly decreases compared to DenseFuse and RFN-Nest, but compared to FusionGAN and four other traditional fusion methods, the metric values are improved by about 26.99%, 49.58%, 37.08%, 44.06%, and 42.78%, with high contrast of the fusion image consistent with the histogram comparison. For the EN and SCD metrics, the proposed method achieves the optimal and second optimal values, respectively, and can extract more source image information. According to the VIF index, the method proposed in this article improved image quality by about 17.49% compared to the DenseFuse method, about 92.64% compared to the FusionGAN method, and about 10.69% compared to the RFN-Nest method. The fused image has a high visual fidelity. In terms of the MI index, the method proposed in this article improved it by about 3.86% compared to the DenseFuse method, about 9.46% compared to the FusionGAN method, and about 14.65% compared to the RFN-Nest method. The fused image retains more information from the source images. The above analysis shows that the four traditional methods, AVG, LP, DWT, and FSDP, have strong performance in terms of structural similarity and image fidelity, but they have low image contrast and lack texture details. They can be used in special fusion scenes that require high structural similarity. The FusionGAN method tends to retain infrared image information; the DenseFuse method produces fusion images that tend to contain visible light information, with high contrast and visual fidelity, and contain more information, but the texture details at the edges are coarse, and some detailed features are submerged. RFN-Nest performs best in terms of information retention, but the details are presented in a general way, with some areas appearing blurry. Overall, our method is slightly superior to the other seven methods in terms of comprehensive performance and can meet the application requirements of more scenarios. A comparison chart of the spatial frequency (SF), entropy(EN), sum of the correlation of differences (SCD), average gradient (AG), standard deviation (SD), structural similarity index measurement (SSIM), edge preservation degree (\(Q_{F}^{A/B}\)), peak signal-to-noise ratio