## 1 Introduction  
Infrared sensors generate images by capturing the thermal radiation information of objects. Even under extreme conditions, harsh weather, and partial occlusion, they can effectively highlight significant targets. However, infrared images cannot provide sufficient environmental information such as texture details and environmental illumination. In contrast, visible light sensors generate images by capturing the light that interacts with objects, including reflection, diffusion, and transmission. This interaction results in rich texture detail information, making visible images more consistent with human visual perception. The fusion of infrared and visible light images not only solves the problems of blurred backgrounds and missing details of infrared sensors but also overcomes the dependence of visible light sensors on environmental conditions, constructing imageswith both thermal radiation sources and multispectral information. The complementary characteristics of infrared images and visible images have enabled the fusion technology of these two types of images to be widely applied in engineering technology and scientific research [1]. Traditional image fusion algorithms typically perform activity level measurements in the spatial or transform domain and manually design fusion rules to achieve image fusion. Traditional image fusion frameworks mainly include fusion frameworks based on multi-scale transformation [2], sparse representation [3], subspace [4], saliency [5], and variational models [6]. Although existing traditional image fusion algorithms can produce relatively satisfactory results in most cases, there are still some problems that hinder their further development. First, existing methods usually use the same transformation or representation to extract features from source images but do not consider the inherent feature differences of multi-modal images. Second, manually designed activity level measurements and fusion rules cannot adapt to complex fusion scenarios. In order to pursue better fusion performance, the design of activity level measurements and fusion rules becomes more and more complex, limiting the practical application of traditional methods [7]. Consequently, image fusion algorithms based on deep learning have emerged, highlighting their numerous advantages over traditional algorithms. Existing deep learning-based image fusion algorithms primarily aim to address three critical issues in image fusion: feature extraction, feature fusion, and image reconstruction. To tackle these key problems and improve upon the shortcomings of traditional algorithms, researchers have designed various deep learning networks to achieve higher efficiency and better performance. Depending on the network architecture adopted, these algorithms can be categorized into three types: image fusion frameworks based on autoencoders (AE), convolutional neural networks (CNN), and generative adversarial networks (GAN). In AE-based methods, networks pre-trained on large datasets are commonly used to address the issues of feature extraction and image reconstruction. However, the fusion strategies for integrating deep features extracted from different modal images are still manually designed, which restricts the performance of these methods [8]. Compared to AE-based methods, CNN-based methods achieve end-to-end feature extraction, feature fusion, and image reconstruction by designing loss functions and network structures. Nevertheless, these methods often employ a simple serial structure and fail to consider the inherent features of different modal images [9]. GAN-based methods model the image fusion problem as an adversarial game between the generator and the discriminator. The generator aims to blend the underlying distributions of the source images into the fused image, while the discriminator determines whether the input is a source image or a fused image. This process enables the generator to produce a fused image that contains more features of the source images.