IAIFNet: An Illumination-Aware Infrared and Visible Image Fusion Network
This addresses the issue of poor target prominence in fused images for downstream vision tasks, but it is incremental as it builds on existing fusion methods with illumination awareness.
The paper tackled the problem of infrared and visible image fusion in low-light environments by proposing IAIFNet, which enhances illumination and integrates salient features, resulting in outperforming five state-of-the-art methods.
Infrared and visible image fusion (IVIF) is used to generate fusion images with comprehensive features of both images, which is beneficial for downstream vision tasks. However, current methods rarely consider the illumination condition in low-light environments, and the targets in the fused images are often not prominent. To address the above issues, we propose an Illumination-Aware Infrared and Visible Image Fusion Network, named as IAIFNet. In our framework, an illumination enhancement network first estimates the incident illumination maps of input images. Afterwards, with the help of proposed adaptive differential fusion module (ADFM) and salient target aware module (STAM), an image fusion network effectively integrates the salient features of the illumination-enhanced infrared and visible images into a fusion image of high visual quality. Extensive experimental results verify that our method outperforms five state-of-the-art methods of fusing infrared and visible images.