Abstract: Fusion of infrared and visible images can take into account both details and targets in complex scenes.Still,the diffe-rence in day and night illumination leads to an inherent conflict in the focus of the two types of images:during the daytime,the texture structure of the visible image should be preserved,while at night,it relies on the infrared image to highlight the target,and it is difficult for a single network to optimize under different illumination at the same time,which results in degradation of the performance.The research objective of this paper is to resolve the policy conflict across different illumination conditions and develop a robust fusion method that can adapt to changes in illumination.To this end,this paper proposes a conditionalized dual-network framework that adapts the assignment of day/night scenes through light sensing and designs complementary information extraction and soft switching mechanisms during the fusion process to cope with continuous light transitions smoothly.Experiments on the MSRS,M3FD,and TNO datasets demonstrate that the method outperforms in both structural fidelity and target saliency metrics,significantly alleviating the performance bottleneck caused by day-night conflicts.The results verify that light adaptive modelling is an important way to improve the robustness of the fusion of infrared and visible images.

Key words: Visible images, Infrared images, Image fusion, Light adaptation, Dual fusion network, Guided complementary information extraction