SLFusion: A Structure-Aware Infrared and Visible Image Fusion Network for Low-Light Scenes

Infrared and visible image fusion is an image enhancement technique that generates a single image with rich textures and significant objectives in a variety of scenarios, providing great convenience for human discrimination and computer recognition. However, in low-light environments, low-intensity visible images tend to blur valuable information, and these details are often ignored during image fusion, resulting in the loss of important information. Although existing methods take into account the damage of low illumination and highlight the illumination in the fusion process, a large amount of structural information is lost in the process of adjusting illumination, resulting in the lack of texture details and poor performance in high-level vision tasks. To address the above challenges, this paper proposes a structure-aware image fusion method for low illumination scenes, called SLFusion, which enhances the illumination while reducing the loss of structural information, leading to a fused image with richer texture details. We first design an illumination enhancement module to separate the degraded illumination from the scene information in the visible image, and mine more details from the low-intensity regions. Based on the fact that image edge information has a good capability of modeling structures, we design an edge extraction network for low-light visible images to model the structural information, which can accurately highlight important structural information and inject it into the fusion image. The proposed method produces fusion results that not only have good visual perception, but also minimize the loss of structural information. Extensive experiments on benchmark datasets demonstrate that the proposed method outperforms state-of-the-art (SOTA) methods in terms of visual quality, quantitative metrics as well as advanced vision tasks.