A Robust Infrared and Visible Image Registration Method for Dual-Sensor UAV System
Yan Mo, Xudong Kang, Shuo Zhang, Puhong Duan, Shutao Li
Abstract
Single-modal image registration methods are generally not feasible for visible and infrared images. Besides, multi-modal image registration methods still suffer from uneven distribution of extracted features, low repeatability, and ambiguous features. To address these issues, a coarse-to-fine infrared and visible image registration approach for dual sensor UAV imaging system is proposed, which is resilient to the difference of focal lengths and field of view. First, in the coarse registration step, the infrared image is transformed to the same scale as the visible image by using the similarity transformation. This operation makes the proposed method robust to the variation of field of view. Then, the feature point pairs are initialized using feature detectors in the infrared image’s blocked phase congruency feature map. Next, the feature point pairs are optimized by estimating the offset based on the relationship between the constructed feature descriptors. Finally, using elastic deformation, the pixel-level registered infrared image is obtained. Extensive experiments demonstrate the superior performance of the proposed coarse-to-fine image registration methodology in the real infrared-visible image pairs. The code and dataset are available at https://drive.google.com/drive/folders/1mpUWwHUbKTrBdOrNMNRRnuJclDUAC7nU?usp=sharing.