An Automatic Registration Algorithm of Infrared and Visible Images Based on Optimal Mapping of Edges
基于边缘最优映射的红外和可见光图像自动配准算法

To aim at the difficulties in extracting and matching of corresponding features between infrared and visible images of the same scene, an automatic registration algorithm based on optimal mapping of edges in multi-scale space is proposed. In the scale space from coarse to fine, the affine model and projective model are selected as the spatial transformation models between unregistered images and references images, respectively. At each scale layer, edge structures in both images are first extracted using phase congruency method, then the extracted binary edges of the unregistered image are mapped onto the strength edge map of the reference image based on the corresponding transformation model, and then the parallel genetic algorithm is used to search for a set of global optimal model parameters, which maximizes the structure similarity measure between the two images. After optimization at each scale, the global optimal model parameters are locally refined using Powell algorithm. The experimental results show that the proposed algorithm can take advantage of visual similar structures between images and realize automatic registration of infrared and visible images efficiently.