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Since the differences in viewing range, resolution and relative position, themulti-modality sensing module composed of infrared and visible cameras needs tobe registered so as to have more accurate scene perception. In practice, manualcalibration-based registration is the most widely used process, and it isregularly calibrated to maintain accuracy, which is time-consuming andlabor-intensive. To cope with these problems, we propose a scene-adaptiveinfrared and visible image registration. Specifically, in regard of thediscrepancy between multi-modality images, an invertible translation process isdeveloped to establish a modality-invariant domain, which comprehensivelyembraces the feature intensity and distribution of both infrared and visiblemodalities. We employ homography to simulate the deformation between differentplanes and develop a hierarchical framework to rectify the deformation inferredfrom the proposed latent representation in a coarse-to-fine manner. For that,the advanced perception ability coupled with the residual estimation conduciveto the regression of sparse offsets, and the alternate correlation searchfacilitates a more accurate correspondence matching. Moreover, we propose thefirst ground truth available misaligned infrared and visible image dataset,involving three synthetic sets and one real-world set. Extensive experimentsvalidate the effectiveness of the proposed method against thestate-of-the-arts, advancing the subsequent applications.

Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration