  Recent learning-based image fusion methods have marked numerous progress in
pre-registered multi-modality data, but suffered serious ghosts dealing with
misaligned multi-modality data, due to the spatial deformation and the
difficulty narrowing cross-modality discrepancy. To overcome the obstacles, in
this paper, we present a robust cross-modality generation-registration paradigm
for unsupervised misaligned infrared and visible image fusion (IVIF).
Specifically, we propose a Cross-modality Perceptual Style Transfer Network
(CPSTN) to generate a pseudo infrared image taking a visible image as input.
Benefiting from the favorable geometry preservation ability of the CPSTN, the
generated pseudo infrared image embraces a sharp structure, which is more
conducive to transforming cross-modality image alignment into mono-modality
registration coupled with the structure-sensitive of the infrared image. In
this case, we introduce a Multi-level Refinement Registration Network (MRRN) to
predict the displacement vector field between distorted and pseudo infrared
images and reconstruct registered infrared image under the mono-modality
setting. Moreover, to better fuse the registered infrared images and visible
images, we present a feature Interaction Fusion Module (IFM) to adaptively
select more meaningful features for fusion in the Dual-path Interaction Fusion
Network (DIFN). Extensive experimental results suggest that the proposed method
performs superior capability on misaligned cross-modality image fusion.
