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