# Dense-FG: A Fusion GAN Model by Using Densely Connected Blocks to Fuse Infrared and Visible Images
## 4 Experiments and Results
### Comparative Experiment  
To validate the efficacy of the approach presented in this article, we conducted a comparative analysis with seven additional fusion methods: Average gradient (AVG), Laplacian pyramid (LP), filter subtract decimate pyramid (FSDP), discrete wavelet transform (DWT), FusionGAN, Densefuse, and RFN-Nest. In the experiment, we applied these seven fusion methods to 10 randomly selected pairs of infrared and visible source images. Figure 4 shows the fusion results of different methods on six pairs of source images, which are \"airplane_in_trees\", \"soldier_in_trench_1\", \"Sandpath\", \"Fennek01_005\", \"Kaptein_1123\", and \"Nato_camp\" from left to right. The sizes of the registered source image pairs are: \(595\times 328\), \(768\times 576\), \(575\times 475\), \(749\times 551\), \(620\times 450\), \(360\times 270\). The output images of various fusion methods are shown in Figure 4. We used red boxes to mark the infrared radiation information and blue boxes to mark the visible texture information. Based on subjective analysis, traditional methods such as AVG, LP, FSDP, and DWT can effectively fuse the basic information of the source images to some extent. However, the fused images obtained have poor infrared target saliency and low richness in texture details. In addition, the overall tone of the fused images is dark and does not enable a good subjective visual perception. Although the FusionGAN method can obtain fused images with significantly brighter infrared targets than the other methods,its feature extraction capability is insufficient, the texture details of the fused images are missing, and it cannot effectively fuse visible image information. The DenseFuse method effectively extracts prominent features from the source images through the encoding network, resulting in good target saliency of the fused images. However, due to the insufficient use of source image information during autoencoder training, the preservation of complementary information between source images is reduced, and the clarity of fused image details is reduced. The RFN-Nest method uses fusion learning through encoding-decoding and residual networks to generate images with clear infrared target contours, but lacks image detail information, resulting in blurriness of fine detail areas and high image noise. In this paper's method, dense connection blocks are introduced in both the generator and discriminator network structures to better utilize the source image information and retain more complementary  
Figure 4: Comparison of fusion results of different algorithms. We label the infrared radiation information with a red box and the visible texture information with a blue box for clear comparison. information. The texture details are more abundant and clearer, and the important features of the image pair have higher fusion quality and better visual effects. The histograms of the output images for various fusion algorithms are shown in Figure 5. The pixel distribution information of the images can be clearly seen from the histograms. By comparison, the histograms of the fusion results of the four traditional methods, AVG, LP, FSDP, and DWT, are almost identical in shape, with discontinuous pixel distribution, resulting in brightness jumps and missing information in the images. The pixel peak values of the images generated by the four deep learning fusion methods, FG, DenseFuse, RFN-Nest, and Dense-FG, are all between those of the infrared and visible images, but their shapes are different. The fusion result of FG is closer to the infrared image, indicating that the infrared target information is more prominent in the FG fusion image, while the visible information is weaker. The histograms of the fusion results of DenseFuse, RFN-Nest, and Dense-FG are similar in shape, and they are closer to the visible image. The pixel frequency of the DenseFuse histogram is prominent and layered, indicating that the visible details in the DenseFuse fusion image are abundant, and the image details are prominent.