# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Methods Based on Deep Learning
#### 2.6.2 Methods Based on Generating Adversarial Network  
The idea of generative adversarial networks (GAN), an artificial intelligence algorithm created to address generative modeling issues, was initially put forth in 2014 by [PERSON] et al. [54] From estimated probability distributions, generative adversarial networks can produce new examples. As soon as it was put forth, it garnered considerable interest in the deep learning community. The generator and discriminator are the two components of  
Figure 7: Image fusion frame diagram based on CNN. the generative adversarial network. A discriminator is a binary whose inputs are genuine data and sample data generated by the generator. During the training phase, the generator can utilize random noise to make a fresh data sample, but it must provide real photos as much as possible to fool the discriminator. The discriminator makes every effort to distinguish the true picture from the one created by the generator. Finally, after extensive training, you may produce images using the trained generator. Figure 8 depicts the GAN-based framework for picture fusion. [PERSON] et al. [55] used generative adversarial networks for infrared and visible image fusion for the first time, and they proposed a new method to fuse these two types of information, called FusionGAN. The generator primarily creates the fusion image using the additional visible light gradient and main infrared intensity in this manner. The discriminator primarily increases the fused image's clarity and adds more details to the visible portion of the image. FusionGAN is an end-to-end model that forgoes the conventional method of manually creating intricate activity-level measurement and fusion rules in order to prominently display objectives and specifics. However, the convolution procedure consumes too much computer space, and the FusionGAN approach cannot maintain the crucial information of the source picture simultaneously in the fusion process. A brand-new end-to-end network architecture based on generative adversarial networks, known as FLGC-Fusion GAN, was proposed by [PERSON] et al. [56]. Learnable group convolution in the generator enhances the model's effectiveness, conserves computational resources, and enables us to better balance the model's accuracy and speed. GAN was expanded by [PERSON] et al. [51] to include multiple discriminators, and an end-to-end multi-discriminator Wasserstein Generative Adversarial Network (MD-WGAN) was developed. According to this framework, the fused image can keep the texture information provided by the second discriminator as well as the infrared intensity and detail information provided by the first discriminator. To preserve more textures in visible images, a texture loss mechanism based on local binary mode was also designed. An attention-based adversarial network generation method, called AttentionFGAN, was put out by [PERSON] et al. [57] for the fusion of visible and infrared images. In order to capture extensive spatial information, the authors incorporate multi-scale attention mechanisms into the generator and discriminator of GAN. This allows the discriminator to concentrate on the background features of the visible image and the foreground target information of the infrared image. An infrared and visible image fusion generative admixture network (SSGAN) based on semantic segmentation was proposed by [PERSON] et al. [58] It can distinguish between the foreground and background of source images through semantic masking and can take into account both low-level features of infrared and visible images and high-level semantic information. This network's generator uses a dual-encoder, single-decoder design that allows it to separate foreground  
Figure 8: Image fusion framework based on GAN network. and background characteristics using various encoder pathways. Additionally, semantic segmentation is used to create the discriminator's input image. The foreground of the infrared image and the background of the visible image are combined to create the image.