基于局部结构张量-互信息的多模态图像配准

针对红外与可见光图像难以提取特征点实现配准的问题，提出一种基于边缘结构特征的红外与可见光图像配准算法。首先通过优化的显著性算法增强红外图像的结构特征；其次利用相位一致性提取红外和可见光图像的稳定边缘结构；然后提取边缘结构的ORB（oriented FAST and rotated BRIEF）特征点；最后结合KNN（K-nearest neighbor）算法和余弦相似度对匹配特征点进行筛选，并应用RANSAC（random sample consensus）算法进行提纯。实验表明，该算法能够克服灰度差异的影响，具有较高的配准精度和效率，有助于实现红外与可见光图像的配准。

Visible and infrared image registration based on region features and edginess

Here, a registration algorithm based on edge structure features is proposed to solve the difficulty of extracting feature points from infrared and visible images. First, the structural features of infrared images are enhanced using an optimized saliency algorithm. Second, we extract the stable edge structures of the infrared and visible images using a phase consistency algorithm. Further, the ORB feature points are extracted from the edge structures. Finally, the KNN algorithm and cosine similarity are combined to filter the matching feature points, and the random sample consensus (RANSAC) algorithm is used for purification. Experimental results show that the algorithm overcomes the influence of grayscale differences between infrared and visible images. In addition, it achieves a high registration accuracy and efficiency, which is conducive to the registration of infrared and visible images.