Harmonized Domain Enabled Alternate Search for Infrared and Visible Image Alignment

Infrared and visible image alignment is essential and critical to the fusion and multi-modal perception applications. It addresses discrepancies in position and scale caused by spectral properties and environmental variations, ensuring precise pixel correspondence and spatial consistency. Existing manual calibration requires regular maintenance and exhibits poor portability, challenging the adaptability of multi-modal application in dynamic environments. In this paper, we propose a harmonized representation based infrared and visible image alignment, achieving both high accuracy and scene adaptability. Specifically, with regard to the disparity between multi-modal images, we develop an invertible translation process to establish a harmonized representation domain that effectively encapsulates the feature intensity and distribution of both infrared and visible modalities. Building on this, we design a hierarchical framework to correct deformations inferred from the harmonized domain in a coarse-to-fine manner. Our framework leverages advanced perception capabilities alongside residual estimation to enable accurate regression of sparse offsets, while an alternate correlation search mechanism ensures precise correspondence matching. Furthermore, we propose the first ground truth available misaligned infrared and visible image benchmark for evaluation. Extensive experiments validate the effectiveness of the proposed method against the state-of-the-arts, advancing the subsequent applications further. Code and dataset are available at https://github.com/Jzy2017/HR4IR.