<p>Existing methods for infrared and visible image fusion (IVIF) effectively highlight thermal targets but often fail to accurately and fully represent scene information, particularly in low-light environments. This limitation stems from two primary reasons: the modality difference between infrared and visible images, which can distort the fused image when introducing single-channel infrared information, and the ignorance of illumination degradation in the visible image, which can make the fused image appear dark and lacking in scene details. To address these challenges, we propose an efficient feature difference-based IVIF method tailored for low-light conditions. Our method jointly processes low-light image enhancement (LLIE) and IVIF. First, the low-light visible image is adaptively enhanced to produce a bright scene with rich textures and colors. Additionally, we map infrared and visible images into a same high-dimensional feature space and present a feature difference mechanism to ensure the fused image contains all visible image information while retaining only infrared image unique information. This avoids the risk of distortion from introducing redundant infrared information. We also propose a three-channel fusion coefficient map and strengthen the correlation between infrared and visible modalities, converting infrared unique information into a three-channel format. This resolves distortion issues from modality differences during fusion, ensuring accurate scene details. To achieve low computational cost and high efficiency during testing, we employ a multi-stage training strategy, enabling a single lightweight module to produce stable fusion results. Extensive experiments demonstrate that our method surpasses state-of-the-art (SOTA) methods in terms of fusion quality and efficiency.</p>