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.

提出了一种考虑低光环境照明条件的光学和红外图像融合网络，利用自适应差分融合模块和显著目标感知模块，将提升过照明的红外和可见图像的显著特征有效地融合，生成高质量的融合图像，并验证其优于五种最先进的红外和可见图像融合方法。