# Dense-FG: A Fusion GAN Model by Using Densely Connected Blocks to Fuse Infrared and Visible Images
## 2 Literature Survey
### Research Status of Infrared and Visible Image Fusion Based on Deep Learning  
Deep learning has exhibited outstanding results in image processing and has been extensively utilized for the integration of infrared and visible images. In 2016, [PERSON] et al. [22] proposed a fusion method based on convolutional neural networks (CNN) for infrared and visible images. They employed a Siamese convolutional network to generate weight maps, extract pixel information from two source images, and combine the extracted information in a multi-scale way through an image pyramid.  
To address the issue of most deep learning-based methods directly using deep features without feature extraction or processing, according to [PERSON] et al. [23] residual networks and zero-phase component analysis (ZCA) can be used to integrate such data. This approach utilizes residual networks for extracting profound features and normalizes these features utilizing ZCA, leading to an enhancement in the quality of the reconstructed fusion image.  
To further improve the quality of the fusion image, [PERSON] et al. [24] implemented image fusion using encoding and decoding networks and introduced dense connection blocks in the encoding network to extract more source image features.  
In 2019, [PERSON] et al. [25] introduced a technique for combining infrared and visible images utilizing generative adversarial networks (GAN), which they named FusionGAN. This method continuously improves its generation and discrimination capabilities through a generator and a discriminator playing an adversarial game, obtaining fusion images containing rich source-image information.  
In 2020, [PERSON] [26] played an adversarial game with one generator and two discriminators to preserve a balance between the infrared radiation data and visible texture features and extended it to multi-resolution image fusion.  
In 2021, [PERSON] et al. [27] used a classifier to classify source images to address the limitation of manually setting fusion rules during the fusion process. The classification result was quantified as an important coefficient and presented as a saliency map. The fusion of various feature maps generates a fusion image, and even simple networks produce good results. [PERSON] et al. [28] suggested a fusion approach based on aggregated residual dense networks for infrared and visible images, which automatically assesses the information preservation of the source image, extracts hierarchical features, and achieves effective fusion. [PERSON] et al. [29] suggested a fusion strategy that utilizes a combined texture map and an adaptive guided filter to generate multiple decision maps. The fusion image performed well in the subjective evaluation and quantitative indicators. To address the issue of non-prominent infrared targets in fusion images, [PERSON] et al. [30] incorporated an attention mechanism into the generator and discriminator based on GAN to obtain fusion images with prominent targets and rich details.  
The fusion procedure of infrared and visible images based on deep learning mentioned above is mainly achieved by modifying the image fusion framework, configuring the fusion network architecture, and formulating loss functions to restrict the fusion process. Various design concepts and fusion regulations have distinct impacts on fusion outcomes. Further research can be conducted to enhance the quality of the fused image by exploring the equilibrium between the luminance of the infrared target and the texture particulars of the visible light.