Authors
Yiming Sun, Zifan Ye, Qinghua Hu, Pengfei Zhu
Published in
IEEE transactions on neural networks and learning systems. Volume PP. Sep 18, 2026. Epub Sep 18, 2026.
Abstract
Multimodal 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 models (SSMs) have achieved satisfactory 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 SSM for multimodal 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 cross-modal discrepancy features and integrating them in a well-balanced manner, DIFF-MF effectively fuses complementary multimodal information. Experimental results on the driving scenarios and low-altitude unmanned aerial vehicle (UAV) datasets demonstrate that our method outperforms existing approaches in both visual quality and quantitative evaluation.
PMID:
42758952
Bibliographic data and abstract were imported from PubMed on 19 Sep 2026.
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