Image fusion combines images from multiple domains into one image, containing complementary information from source domains. Existing methods take pixel intensity, texture and high-level vision task information as the standards to determine preservation of information, lacking enhancement for human perception. We introduce an image fusion method, Hierarchical Perception Fusion (HPFusion), which leverages Large Vision-Language Model to incorporate hierarchical human semantic priors, preserving complementary information that satisfies human visual system. We propose multiple questions that humans focus on when viewing an image pair, and answers are generated via the Large Vision-Language Model according to images. The texts of answers are encoded into the fusion network, and the optimization also aims to guide the human semantic distribution of the fused image more similarly to source images, exploring complementary information within the human perception domain. Extensive experiments demonstrate our HPFusoin can achieve high-quality fusion results both for information preservation and human visual enhancement.

本研究解决了现有图像融合方法在增强人类感知方面的不足，提出了一种新的图像融合方法——分层感知融合（HPFusion）。该方法利用大型视觉语言模型引入分层的人类语义先验，优化融合图像以更好地满足人类视觉系统的需求，实验结果表明HPFusion在信息保留和视觉增强方面均表现出色。

基于分层人类感知的红外与可见光图像融合