DIFF-MF: A Difference-Driven Channel-Spatial State Space Model for Multi-Modal Image Fusion

• URL: http://arxiv.org/abs/2601.05538v1
• Date: Fri, 09 Jan 2026 05:26:54 GMT
• Title: DIFF-MF: A Difference-Driven Channel-Spatial State Space Model for Multi-Modal Image Fusion
• Authors: Yiming Sun, Zifan Ye, Qinghua Hu, Pengfei Zhu,
• Abstract summary: Multi-modal image fusion aims to integrate complementary information from multiple source images to produce high-quality fused images with enriched content.<n>We propose DIFF-MF, a novel difference-driven channel-spatial state space model for multi-modal image fusion.<n>Our method outperforms existing approaches in both visual quality and quantitative evaluation.
• Score: 51.07069814578009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal image fusion aims to integrate complementary information from multiple source images to produce high-quality fused images with enriched content. Although existing approaches based on state space model have achieved satisfied performance with high computational efficiency, they tend to either over-prioritize infrared intensity at the cost of visible details, or conversely, preserve visible structure while diminishing thermal target salience. To overcome these challenges, we propose DIFF-MF, a novel difference-driven channel-spatial state space model for multi-modal image fusion. Our approach leverages feature discrepancy maps between modalities to guide feature extraction, followed by a fusion process across both channel and spatial dimensions. In the channel dimension, a channel-exchange module enhances channel-wise interaction through cross-attention dual state space modeling, enabling adaptive feature reweighting. In the spatial dimension, a spatial-exchange module employs cross-modal state space scanning to achieve comprehensive spatial fusion. By efficiently capturing global dependencies while maintaining linear computational complexity, DIFF-MF effectively integrates complementary multi-modal features. Experimental results on the driving scenarios and low-altitude UAV datasets demonstrate that our method outperforms existing approaches in both visual quality and quantitative evaluation.

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