Infrared and Visible Image Fusion Using a State-Space Adversarial Model with Cross-Modal Dependency Learning

Infrared and visible image fusion plays a critical role in multimodal perception systems, particularly under challenging conditions such as low illumination, occlusion, or complex backgrounds. However, existing approaches often struggle with global feature modelling, cross-modal dependency learning, and preserving structural details in the fused images. In this paper, we propose a novel adversarial fusion framework driven by a state-space modelling paradigm to address these limitations. In the feature extraction phase, a computationally efficient state-space model is utilized to capture global semantic context from both infrared and visible inputs. A cross-modality state-space architecture is then introduced in the fusion phase to model long-range dependencies between heterogeneous features effectively. Finally, a multi-class discriminator, trained under an adversarial learning scheme, enhances the structural fidelity and detail consistency of the fused output. Extensive experiments conducted on publicly available infrared–visible fusion datasets demonstrate that the proposed method achieves superior performance in terms of information retention, contrast enhancement, and visual realism. The results confirm the robustness and generalizability of our framework for complex scene understanding and downstream tasks such as object detection under adverse conditions.