# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Methods Based on Deep Learning
#### 2.6.3 Methods Based on Autoencoder  
In order to transform high-dimensional data into low-dimensional representation, the infrared and visible image fusion approach based on an autoencoder uses a three-layer network of an input layer, a hidden layer, and an output layer. There are typically three steps, which are as follows: The source picture's features are first extracted using an encoder, and then various source image features are combined using a fusion approach. The decoder then reconstructs the fusion image. Figure 9 depicts the autoencoder-based image fusion framework.  
The fusing of multiple exposure photos was made possible in 2017 by [PERSON] et al. [59] who first suggested a unique image fusion architecture based on unsupervised deep learning (DeepFuse). The framework consists of two coding network layers and three decoding network layers; first, convolutional neural networks are used to extract the feature maps from the original image; next, the feature maps in each dimension are fused; and finally, the reduction of the decoding layer is used to produce a fused image. The last layer of the coding network is solely used for calculation in this method, which has a positive outcome but runs the risk of losing useful information in the middle layer. Based on this issue, [PERSON] et al. [60] created a complex deep learning network in 2018. Convolutional layers, fusion layers, and dense blocks are combined in the network's coding network, where the output of each layer is coupled to the output of the previous layer. Two fusion layers are created to combine these features, and this structure can extract additional beneficial source image features throughout the coding phase. A double-branch automatic encoder serves as the foundation of [PERSON] et al.'s [61] double-branch network. The authors extract from the encoder the detailed and semantic information of the infrared image and the visible image, respectively. The fusion layer provides the fusion features. The fusion image is then obtained by reconstructing the fusion feature in the decoder. A brand-new self-supervised feature adaptive architecture for infrared and visible image fusion was put forth by [PERSON] et al. [62]. The original picture information is retained in the first stage using the self-supervised technique in various channels of the coding region; in the second stage, the decoder is replaced with a fusion enhancement module; and finally, a detailed enhanced fusion image is created. An infrared and visible image fusion network (TCPMFNet) using a composite autoencoder and parallel variable-convolution fusion approach was proposed  
Figure 9: Image fusion framework based on autoencoder.  
by [PERSON] et al. [63] The fusion network designs a transformer-convolution parallel hybrid fusion method with outstanding feature fusion performance based on the autoencoder (AE) structure, using the composite autoencoder to encode the rich features of the source picture pairs. [PERSON] et al. [64] proposed a fusion framework based on self-supervised learning (SSL-WAEIE), designed a weighted autoencoder (WAE) to extract multilevel features that need to be fused in source images, and then further constructed a convolutional information exchange network (CIEN), which can more conveniently complete the fusion contribution estimation of source images.