# AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion
Four convolutional layers with shared weights are then implemented to extract deeper IR and visible image features, using leaky rectified linear unit (LeakyReLu) activations within each layer of the feature extractor. These extracted features are then concatenated and supplied as input to the image reconstructor, composed of five convolutional layers. This module works towards integrating common and complementary information to produce a fused image. Each of the convolutional layers, except the last one, has a kernel size of \(3\times 3\), while the last layer has a kernel size of \(1\times 1\). LeakyReLu activations are used in most of the layers, with the last layer adopting the Tanh activation. Given that late image fusion merges multiple images, it can lead to distorted images if not carefully performed, and fusion occurs in the later part of the network, resulting in a relatively weak processing ability in the image reconstruction module. Thus, several limitations should be addressed in the final fusion effect. Based on the aforementioned studies, IR and visible image fusion models can be readily developed using end-to-end training methods supported by a large amount of training data. Generally speaking, various complex image feature representations, extraction designs, and fusion rules that heavily rely on human expertise, are replaced by numerous network parameters, whose values are determined mainly by the training dataset without the need for human intervention. Therefore, fusion models based on deep learning are relatively easy to design and use. In these methods, the network architecture design and the combination of loss functions play a vital role in improving performance. The network architecture design determines the potential ability to extract features from IR and visible light images and the subsequent fusion, while the choice of loss function determines whether the performance of the fused image can be optimized. However, there is still room for improvement in both network architecture and loss functions in current methods. In this work, we present an adjacent feature shuffle combination network (AFSFusion) as a solution to maximize the preservation of complementary information in both IR and visible images, as well as to obtain fused images that display the highest quality and resolution across various objective evaluation metrics. The primary contributions of our proposed network are as follows:  
* Our AFSFusion network differs from the resolution transformation of the UNet [21], wherein the network is specifically designed to optimize image feature processing for image fusion. We establish a hybrid fusion network to achieve fusion by scaling up and down several feature channels based on the improved UNet-like architecture. The first half of the network uses the adjacent feature shuffle-fusion (AFSF) module. This module fuses the features of each adjacent convolutional layer several times using shuffle operations, resulting in the full interaction of the input feature information. Consequently, information loss during feature extraction and fusion is significantly reduced, and the complementarity of feature information is enhanced. The proposed network architecture effectively integrates complementary features using the AFSF module, achieving a balanced and collaborative approach to feature extraction, fusion, and modulation. This method is typically not found in traditional methods, and it has resulted in improved fusion performance. These changes allow the proposed network to achieve excellent performance in terms of subjective and objective evaluation.