DICFusion: Infrared and Visible Image Fusion via a Deep Integrated and Semantic-Coordinated Network

Infrared-visible image fusion (IVF) aims to integrate complementary information from infrared and visible sensors into a single, more informative representation. However, achieving both visual clarity and semantic consistency in the fused results remains a critical challenge, particularly for real-world applications like scene understanding. To address this, we propose DICFusion, a deep integrated and semantic-coordinated network, tailored for perceptually and semantically enriched infrared-visible fusion tasks. Firstly, the DICFusion employs a novel modality-aware fusion strategy to integrate infrared and visible modalities into a cohesive feature embedding. Secondly, the framework incorporates hybrid mamba-convolution blocks, which leverage the combined strengths of mamba and convolution neural networks to accurately capture both global context and localized details while maintaining computational efficiency. To mitigate the feature heterogeneity between fusion and downstream tasks, DICFusion adopts a comprehensive framework of deep integration and collaborative optimization. This design utilizes a unified multi-scale encoder to harmonize feature representations, followed by parallel fusion and segmentation branches to enhance both visual quality and task performance. Moreover, a semantic guidance module leveraging cross-attention mechanism is incorporated to refine the semantic consistency of the fused outcomes. Comprehensive experimental evaluations validate the performance and efficiency of DICFusion, demonstrating its superiority over contemporary state-of-the-art methods, both in terms of fusion visual quality and downstream task precision. The code is available at https://github.com/fd-qhwang/DICFusion.