Misalignments between multi-modality images pose challenges in image fusion,
manifesting as structural distortions and edge ghosts. Existing efforts
commonly resort to registering first and fusing later, typically employing two
cascaded stages for registration,i.e., coarse registration and fine
registration. Both stages directly estimate the respective target deformation
fields. In this paper, we argue that the separated two-stage registration is
not compact, and the direct estimation of the target deformation fields is not
accurate enough. To address these challenges, we propose a Cross-modality
Multi-scale Progressive Dense Registration (C-MPDR) scheme, which accomplishes
the coarse-to-fine registration exclusively using a one-stage optimization,
thus improving the fusion performance of misaligned multi-modality images.
Specifically, two pivotal components are involved, a dense Deformation Field
Fusion (DFF) module and a Progressive Feature Fine (PFF) module. The DFF
aggregates the predicted multi-scale deformation sub-fields at the current
scale, while the PFF progressively refines the remaining misaligned features.
Both work together to accurately estimate the final deformation fields. In
addition, we develop a Transformer-Conv-based Fusion (TCF) subnetwork that
considers local and long-range feature dependencies, allowing us to capture
more informative features from the registered infrared and visible images for
the generation of high-quality fused images. Extensive experimental analysis
demonstrates the superiority of the proposed method in the fusion of misaligned
cross-modality images.

通过一阶优化，采用跨模态多尺度渐进稠密注册方案改进多模态图像融合性能，通过密集变形场聚合和渐进特征细化来准确估计最终的变形场，并结合 Transformer-Conv-based Fusion 子网络捕捉注册红外和可见图像的信息，实现高质量融合图像的生成。