特征匹配的准确率影响图像配准的精度, 是基于特征配准方法的重点和难点之一。为了解决单向最近邻/次近邻法所导致特征点一对多的误匹配问题, 提出了一种红外和可见光图像的特征双向匹配方法。首先, 对红外图像进行反相和直方图均衡化处理, 增强两类图像的相似性, 提取数量更多重复率高的共有特征；其次, 对提取的 SURF(Speed-up Robust Feature)特征进行双向最近邻/次近邻粗匹配, 确保特征匹配的一致性, 降低误匹配率, 并利用 RANSAC(Random Sample Consensus)算法对特征点进行二次匹配, 实现特征点精确匹配。实验结果表明, 该算法在正确匹配率和配准精度方面都优于传统 SURF的单向最近邻/次近邻匹配方法, 具有有效性。

Feature matching accuracy affects the precision of image registration, which is one of difficult key points for image registration based on features. In order to solve feature points one-to-many mismatching problem caused by the ratio of the closest neighbor and second closest neighbor from one direction, a bidirectional matching method of features for image registration of visible and infrared image is put forward. Firstly, for enhancing the similarity of two images, image reverse and histogram equalization are adopted to process infrared image, so that more consistent features of high repetition rate are extracted. Next, SURF features are matched bilaterally by using the ratio of the closest neighbor and second closest neighbor, to ensure the consistency between feature matching and reduce matching error rate, and then RANSAC is applied to match feature again. Through the two matches, it can realize precise features matching. The experiment results show that the proposed method is better than traditional SURF feature unilateral matching algorithm based on the ratio of the closest neighbor and second closest neighbor in the correct matching ratio and registration accuracy, and the validity of the method suggested is proved.