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As an essential processing step before the fusing of infrared and visibleimages, the performance of image registration determines whether the two imagescan be fused at correct spatial position. In the actual scenario, the variedimaging devices may lead to a change in perspective or time gap between shots,making significant non-rigid spatial relationship in infrared and visibleimages. Even if a large number of feature points are matched, the registrationaccuracy may still be inadequate, affecting the result of image fusion andother vision tasks. To alleviate this problem, we propose a Semantic-Awareon-Demand registration network (SA-DNet), which mainly purpose is to confinethe feature matching process to the semantic region of interest (sROI) bydesigning semantic-aware module (SAM) and HOL-Deep hybrid matching module(HDM). After utilizing TPS to transform infrared and visible images based onthe corresponding feature points in sROI, the registered images are fused usingimage fusion module (IFM) to achieve a fully functional registration and fusionnetwork. Moreover, we point out that for different demands, this type ofapproach allows us to select semantic objects for feature matching as neededand accomplishes task-specific registration based on specific requirements. Todemonstrate the robustness of SA-DNet for non-rigid distortions, we conductextensive experiments by comparing SA-DNet with five state-of-the-art infraredand visible image feature matching methods, and the experimental results showthat our method adapts better to the presence of non-rigid distortions in theimages and provides semantically well-registered images.

SA-DNet: A on-demand semantic object registration network adapting to non-rigid deformation