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Recent learning-based image fusion methods have marked numerous progress inpre-registered multi-modality data, but suffered serious ghosts dealing withmisaligned multi-modality data, due to the spatial deformation and thedifficulty narrowing cross-modality discrepancy. To overcome the obstacles, inthis paper, we present a robust cross-modality generation-registration paradigmfor 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, thegenerated pseudo infrared image embraces a sharp structure, which is moreconducive to transforming cross-modality image alignment into mono-modalityregistration coupled with the structure-sensitive of the infrared image. Inthis case, we introduce a Multi-level Refinement Registration Network (MRRN) topredict the displacement vector field between distorted and pseudo infraredimages and reconstruct registered infrared image under the mono-modalitysetting. Moreover, to better fuse the registered infrared images and visibleimages, we present a feature Interaction Fusion Module (IFM) to adaptivelyselect more meaningful features for fusion in the Dual-path Interaction FusionNetwork (DIFN). Extensive experimental results suggest that the proposed methodperforms superior capability on misaligned cross-modality image fusion.

Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration