# AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion
These aforementioned studies demonstrate that effectively extracting features from both IR and visible images and designing appropriate fusion rules are crucial for addressing the fusion problem through traditional fusion methods. The design of reasonable fusion rules is essential to maximizing the preservation of complementary content between the two images in the output image. However, these tasks rely on manual experience, and the fusion result has significant limitations and cannot adapt to complex scene changes. The enhancement of deep learning techniques [6] over the last decade and their successful application to the field of low-level vision processing have resulted in the development of various types of network models for IR and visible image fusion by researchers in this field [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. Based on the stage of IR and visible image feature fusion, this process can generally be classified into the following three types [20]: (1) early fusion [7; 8; 9], which involves merging the original images at a low-level feature extraction stage. For instance, an end-to-end fusion model using detail-preserving adversarial learning (DPAL) techniques was proposed by [PERSON] et al. [7]. The middle section consisting of Conv+BN+PReLU+Conv+BN repeats five times, where BN represents batch normalization. The back section contains a \(3\times 3\) convolutional layer, a PReLU, and a \(1\times 1\) convolutional layer. DPAL uses an advanced adversarial learning technique that has proved to be highly effective across multiple fields. This approach can be implemented on diverse image data types and can rapidly train high-quality models in a short duration. However, this kind of method tends to produce fused images of a generic nature, as the network architecture lacks appropriate mechanisms to facilitate image feature fusion, ultimately failing to fully leverage the complementary nature of IR and visible images. This inadequate fusion of image features often leads to an excessive redundancy in input feature information, complicating image reconstruction tasks in later stages. (2) Medium fusion [10; 11; 12; 13]. [PERSON] et al. [11] proposed a network model called dense fusion (DenseFuse) for the fusion of IR and visible images, which achieved image fusion by cascading feature maps of multiple images of the input. Compared to other image fusion methods, DenseFuse retains detailed information in the source images, while facilitating superior robustness and scalability. This method reduces the noise and produces a visually appealing output image. However, it has also been observed to lead to a loss of image information and reduced spatial resolution. The applied network architecture, which uses medium-term feature fusion, initially extracts source image features before proceeding with the fusion. Lastly, the fused features are reconstructed into the final fused image using a relatively complex feature reconstruction module. Although the fusion framework in this architecture successfully prevents the introduction of redundant information compared to earlier feature fusion architectures, the feature fusion module is relatively simple and may not fuse features effectively. Consequently, image feature fusion is inadequate, leading to an inferior image reconstruction quality post-fusion. Overall, this approach is not optimal and can be improved. (3) Late fusion [14; 15; 16; 17; 18; 19]. In late fusion, the input images are first processed separately to extract relevant features, and these features are then fused to create a final image. [PERSON] et al. [14] proposed a novel light-awareness-based network designed to manage luminance distribution for salient target retention while preserving texture in the background. This approach fully incorporates complementary and public information within the inherent constraints of light awareness. The initial design involves a \(1\times 1\) convolutional layer to minimize modal discrepancies between the IR and visible images.