SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer

This study proposes a novel general image fusion framework based on cross-domain long-range learning and Swin Transformer, termed as SwinFusion. Extensive experiments on both multi-modal image fusion and digital photography image fusion demonstrate the superiority of our SwinFusion compared to the advanced unified image fusion algorithms and task-specific alternatives.

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• 1 On the one hand, an attention-guided cross-domain module is devised to achieve sufficient integration of complementary information and global interaction.
• 2 More specifically, the proposed method involves an intra-domain fusion unit based on self-attention and an inter-domain fusion unit based on cross-attention, which mine and integrate long dependencies within the same domain and across domains.
• 3 Through long-range dependency modeling, the network is able to fully implement domain-specific information extraction and cross-domain complementary information integration as well as maintaining the appropriate apparent intensity from a global perspective.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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