Improving Misaligned Multi-modality Image Fusion with One-stage
  Progressive Dense Registration
Di Wang Jinyuan Liu Long Ma Risheng Liu Xin Fan
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.