SPGFusion: A Semantic Prior Guided Infrared and Visible Image Fusion Network

Infrared and visible image fusion is an important multimodal image processing task that aims to enhance computer vision performance by effectively fusing infrared and visible images. Although in recent years, many deep learning-based methods for infrared and visible image fusion have emerged. Howeve, most of these methods ignore the important role of semantic information in image fusion. Therefore, this paper proposes a semantic priori guided infrared and visible image fusion network called SPGFusion. It uses an adversarial generative network framework based on semantic priors to guide the infrared and visible image fusion process by combining a semantic feature-aware module and semantic generative adversarial loss. Experimental results demonstrate that the SPG-Fusion method yields more visually appealing fusion results and outperform state-of-the-art image fusion algorithms in visual quality and quantitative evaluation. The source code is available at https://github.com/tianzhiya/SPGFusion.