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.

翻译：红外与可见光图像融合（IVIF）旨在生成同时包含两种图像综合特征的融合图像，有助于下游视觉任务。然而，现有方法很少考虑低光照环境下的照明条件，导致融合图像中的目标往往不够突出。为解决上述问题，我们提出了一种光照感知的红外与可见光图像融合网络，命名为IAIFNet。在框架中，光照增强网络首先估计输入图像的入射光照图。随后，借助所提出的自适应差分融合模块（ADFM）和显著目标感知模块（STAM），图像融合网络将光照增强后的红外与可见光图像的显著特征有效整合成具有高视觉质量的融合图像。大量实验结果表明，我们的方法在红外与可见光图像融合方面优于五种最先进方法。