IAIFNet: An Illumination-Aware Infrared and Visible Image Fusion Network

Infrared and visible image fusion (IVIF) aims to create fused images that encompass the comprehensive features of both input images, thereby facilitating downstream vision tasks. However, existing methods often overlook illumination conditions in low-light environments, resulting in fused images where targets lack prominence. To address these shortcomings, we introduce the Illumination-Aware Infrared and Visible Image Fusion Network, abbreviated by IAIFNet. Within our framework, an illumination enhancement network initially estimates the incident illumination maps of input images, based on which the textural details of input images under low-light conditions are enhanced specifically. Subsequently, an image fusion network adeptly merges the salient features of illumination-enhanced infrared and visible images to produce a fusion image of superior visual quality. Our network incorporates a Salient Target Aware Module (STAM) and an Adaptive Differential Fusion Module (ADFM) to respectively enhance gradient and contrast with sensitivity to brightness. Extensive experimental results validate the superiority of our method over seven state-of-the-art approaches for fusing infrared and visible images on the public LLVIP dataset. Additionally, the lightweight design of our framework enables highly efficient fusion of infrared and visible images. Finally, evaluation results on the downstream multi-object detection task demonstrate the significant performance boost our method provides for detecting objects in low-light environments.