TL;DR
This work proposes EdgeAttnSwin, a framework that reformulates fusion as a three-stage progressive optimization process with explicit causal dependencies, and demonstrates that this method outperforms state-of-the-art approaches on infrared-visible and medical image fusion benchmarks.
Different imaging modalities exhibit inherent discrepancies in intensity characteristics and information representation, resulting in pronounced heterogeneity among multimodal features. When these heterogeneous features are directly learned and fused within a unified representation space, feature coupling may arise, le...
A Boundary-Guided Dual-Perspective Cross-Modal Fusion Network (BDPNet) is proposed to explicitly preserve shallow geometric structures and decouple deep semantic fusion into macroscopic and microscopic perspectives.
Infrared and visible image fusion combines complementary thermal and structural information from the two modalities into a single composite image. Existing methods have two critical limitations: (1) inadequate utilization of visible structural information causes blurred edges, and (2) modality-specific and shared respo...
Multi-modal image fusion integrates complementary information from different modalities to generate a unified representation that is informative for human perception and beneficial to downstream vision tasks. However, existing methods often inefficiently model global features and their cross-modal interaction is insuff...
Multimodal image fusion (MMIF) aims to integrate complementary information from different modalities into a high-quality fused image and support downstream tasks. Recently, feature decomposition has become an important paradigm by separating source images into common and modality-specific unique features. However, exis...
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