# AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion
## 3 AFSFusion Network Model
### Backbone Network  
In theory, state-of-the-art convolutional networks, such as DnCNN [24], DenseNet [25], and ResNet [26] can be employed to construct a backbone network for the AFSFusion model. As previously mentioned, current state-of-the-art image fusion models, comprising early, medium, and late fusion strategies, exhibit varying levels of deficiencies across the three stages of feature extraction, feature fusion, and fused image reconstruction. This results in suboptimal fusion outcomes. Consequently, this study introduces a novel fusion network that can simultaneously extract and fuse intricate image features in the first half of the network. Moreover, fully fused features are further modulated during complex reconstruction processes in the rear half section of the network. Thus, the fusion process is ubiquitous throughout the entire network architecture, ensuring that the ultimate fusion result is of high image quality and accords with human eye perception. Figure 1: The standard U-Net framework. The proposed network utilizes channel expansion and contraction to achieve a balanced and collaborative approach to feature extraction, fusion, and modulation during the process of feature propagation. As shown in Figure 3, the AFFusion network adopts a UNet-like network architecture. The major differences between the AFFusion network and UNet are:  
1. The inputs. It is modified in a dual-channel format to enable the system to receive and process both IR and visible images simultaneously. 2. The elimination of the skip connection. Unlike traditional U-Net, our network utilizes channel expansion and contraction instead of resolution expansion and contraction. The purpose of skip connections is to transfer information between the encoder and decoder, which helps to address issues of information loss and inaccurate semantic information during segmentation. As our network has a relatively high input image resolution and does not use downsampling, feature information is not lost during the fusion process. 3. The addition of the AFF module enhances feature interaction and information transfer capability. In the first half of UNet, the feature channels are sequentially passed from front to back, which limits their information interaction ability. Therefore, the AFF module proposed in this study fuses feature information from the previous convolutional layer with the output of the current convolutional layer, which is then passed to the next convolutional layer, and adds a shuffle operation to facilitate feature interaction. The details of the specific module are presented in Section 3.3. 4. The elimination of the maximum pool layer. Typically, the max pooling layer is employed to reduce dimensionality, compress features, lower network complexity, and decrease the number of required parameters. However, since the AFFusion network primarily utilizes the AFF module to enhance feature channels, the use of only the max pooling layer can result in ambiguity regarding the number of channels. The depth of the network architecture is relatively shallow, while the number of channels is relatively large. The max pooling layer is not utilized to alleviate computational overhead. 5. The replacement of the ReLU layer. To solve the problem in which the ReLU layer cannot handle negative values, it is replaced with a Leaky ReLU layer to address the gradient disappearance problem. Figure 2: The AFFusion network framework. To ensure the efficacy of feature extraction and fusion, the AFSF module is introduced to operate prior to the middle section of the AFSFusion network, enabling the sequential introduction of feature information from the IR and visible images into the subsequent network layers, thereby preserving all feature information. The internal architecture of the AFSF module comprises a double-branching channel and a shuffling operation, facilitating the fusion and interaction of feature information with the double-branching structure, ensuring maximum fusion and transmission of feature information.