  As one of the tasks in Image Fusion, Infrared and Visible Image Fusion aims
to integrate complementary information captured by sensors of different
modalities into a single image. The Selective State Space Model (SSSM), known
for its ability to capture long-range dependencies, has demonstrated its
potential in the field of computer vision. However, in image fusion, current
methods underestimate the potential of SSSM in capturing the global spatial
information of both modalities. This limitation prevents the simultaneous
consideration of the global spatial information from both modalities during
interaction, leading to a lack of comprehensive perception of salient targets.
Consequently, the fusion results tend to bias towards one modality instead of
adaptively preserving salient targets. To address this issue, we propose the
Saliency-aware Selective State Space Fusion Model (S4Fusion). In our S4Fusion,
the designed Cross-Modal Spatial Awareness Module (CMSA) can simultaneously
focus on global spatial information from both modalities while facilitating
their interaction, thereby comprehensively capturing complementary information.
Additionally, S4Fusion leverages a pre-trained network to perceive uncertainty
in the fused images. By minimizing this uncertainty, S4Fusion adaptively
highlights salient targets from both images. Extensive experiments demonstrate
that our approach produces high-quality images and enhances performance in
downstream tasks.
