# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 1 Introduction  
Image fusion is a process whereby images from diverse sensors or modalities are amalgamated into a singular image. This crucial phase in image processing is designed to extract and enhance information from multiple sources, resulting in a composite image that is both more informative and of superior quality. Based on variations in imaging devices, image fusion tasks are primarily classified into three categories: multimodal image fusion, remote sensing image fusion, and digital photography image fusion [1]. Among these, the fusion of infrared and visible images, as a branch of multimodal image fusion, has been a topic of considerable interest. Visible images, which are captured following the imaging principles of the human eye, are rich in color and texture information, but they are susceptible to obstruction and cannot function in low-light or nighttime conditions. Infrared images emphasize the thermal distribution characteristics of targets and are suitable for low-light and adverse weather conditions, but they lack color and texture information. Therefore, the fusion of these two types of images can result in a composite image that possesses enriched information and enhanced visual perception, providing significant assistance in various application areas, such as target detection [2], medical diagnosis [3], and remote sensing localization [4]. Currently, infrared and visible image fusion methods are typically categorized into two types based on the representation learning techniques employed in their algorithms: traditional image fusion algorithms and those based on deep learning. Most traditional fusion algorithms primarily utilize signal processing techniques to accomplish fusion tasks. Traditional image fusion techniques commonly employ multi-scale transformations and methods based on sparse or low-rank representations (SRs/LRRs). Among these, methods based on multi-scale transformations, such as the discrete wavelet transform [5], contourlet transform [6], and shearlet transform [7], are notable. The strength of these methods stems from their capability to extract feature information in the frequency domain, which is unattainable in the spatial domain, which helps enhance the performance of fusion algorithms. However, the efficacy of these algorithms is primarily contingent upon the multi-scale transformation operation, which complicates the task of identifying a suitable transformation applicable to diverse image types. On the other hand, the transformation between spatial and frequency domains not only elevates computational complexity but also results in the loss of crucial image features. Methods based on sparse/low-rank representations (SRs/LRRs), such as SR-HOG [8], DDL [9], JSR [10], and DLRR [11], are applied directly to source images in the spatial domain to extract features, thereby minimizing the loss of feature information typically incurred by transformations between spatial and frequency domains. However, when the source images contain complex information, the performance of these algorithms can drastically decrease. In recent years, the rapid advancement of deep learning technology has ushered in new opportunities for the fusion of infrared and visible images. The introduction of convolutional neural networks and attention mechanisms has broadened the scope of image fusion, surpassing traditional algorithms and giving rise to a variety of deep learning-based methods. [PERSON] et al. [12] introduced the pretrained deep learning network VGG into the image fusion model, significantly improving fusion performance compared to traditional fusion networks. However, since deep learning processing was only added to a few branches and the pretrained structure was not specifically designed for fusion tasks, the features extracted may not necessarily contain complementary information for infrared and visible images. To accomplish feature extraction and image reconstruction, the autoencoder-based image fusion method first pretrains an autoencoder on a sizable dataset.