UniFuse: A Unified All-in-One Framework for Multi-Modal Medical Image Fusion Under Diverse Degradations and Misalignments
Journal:
arXiv
Published Date:
Jun 28, 2025
Abstract
Current multimodal medical image fusion typically assumes that source images
are of high quality and perfectly aligned at the pixel level. Its effectiveness
heavily relies on these conditions and often deteriorates when handling
misaligned or degraded medical images. To address this, we propose UniFuse, a
general fusion framework. By embedding a degradation-aware prompt learning
module, UniFuse seamlessly integrates multi-directional information from input
images and correlates cross-modal alignment with restoration, enabling joint
optimization of both tasks within a unified framework. Additionally, we design
an Omni Unified Feature Representation scheme, which leverages Spatial Mamba to
encode multi-directional features and mitigate modality differences in feature
alignment. To enable simultaneous restoration and fusion within an All-in-One
configuration, we propose a Universal Feature Restoration & Fusion module,
incorporating the Adaptive LoRA Synergistic Network (ALSN) based on LoRA
principles. By leveraging ALSN's adaptive feature representation along with
degradation-type guidance, we enable joint restoration and fusion within a
single-stage framework. Compared to staged approaches, UniFuse unifies
alignment, restoration, and fusion within a single framework. Experimental
results across multiple datasets demonstrate the method's effectiveness and
significant advantages over existing approaches.
