A Bidirectional Iterative and Synergistic Supervision Approach for Infrared–Visible Image Registration

Multimodal image registration techniques, critical for advancing the measurement science of infrared and visible images, focus on aligning the same feature points across diverse modalities to achieve comprehensive and accurate instrumentation. However, it is still challenging to achieve high accuracy and robustness for infrared and visible image registration with complex deformations and significant modal differences. In this study, we propose a consistency-constrained bidirectional registration network (CCBRN), which enhances the stability of the registration process by capturing both forward and backward registration details. Furthermore, we develop a synergistic unimodal and multimodal supervision (SUMS) framework, which is designed for learning and imposing cross- and intramodal registration constraints. Experiments on several benchmark datasets have demonstrated that the proposed method not only improves registration accuracy but also exhibits excellent stability. Specifically, our method achieves 1.382, 2.680, and 2.557 endpoint errors (EPEs) on the RoadScene, FLIR, and MSRS datasets, respectively, resulting in error reductions of 13.24%, 5.20%, and 11.61% over existing methods. The proposed method surpasses existing techniques in handling image registration with complex deformations and significant modal differences, as it effectively extracts the commonality features between the infrared and visible images, thereby improving the registration accuracy.