  Infrared and visible image fusion has been developed from vision perception
oriented fusion methods to strategies which both consider the vision perception
and high-level vision task. However, the existing task-driven methods fail to
address the domain gap between semantic and geometric representation. To
overcome these issues, we propose a high-level vision task-driven infrared and
visible image fusion network via semantic and geometric domain transformation,
terms as HSFusion. Specifically, to minimize the gap between semantic and
geometric representation, we design two separate domain transformation branches
by CycleGAN framework, and each includes two processes: the forward
segmentation process and the reverse reconstruction process. CycleGAN is
capable of learning domain transformation patterns, and the reconstruction
process of CycleGAN is conducted under the constraint of these patterns. Thus,
our method can significantly facilitate the integration of semantic and
geometric information and further reduces the domain gap. In fusion stage, we
integrate the infrared and visible features that extracted from the
reconstruction process of two seperate CycleGANs to obtain the fused result.
These features, containing varying proportions of semantic and geometric
information, can significantly enhance the high level vision tasks.
Additionally, we generate masks based on segmentation results to guide the
fusion task. These masks can provide semantic priors, and we design adaptive
weights for two distinct areas in the masks to facilitate image fusion.
Finally, we conducted comparative experiments between our method and eleven
other state-of-the-art methods, demonstrating that our approach surpasses
others in both visual appeal and semantic segmentation task.
