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

• URL: http://arxiv.org/abs/2205.11876v1
• Date: Tue, 24 May 2022 07:51:57 GMT
• Title: Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration
• Authors: Di Wang, Jinyuan Liu, Xin Fan, Risheng Liu
• Abstract summary: We present a robust cross-modality generation-registration paradigm for unsupervised misaligned infrared and visible image fusion. To better fuse the registered infrared images and visible images, we present a feature Interaction Fusion Module (IFM)
• Score: 59.02821429555375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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