针对 SIFT算法的图像配准耗时长的问题, 提出一种 CSS-SIFT复合图像配准算法。 CSS-SIFT算法首先使用 CSS算法检测图像特征, 然后, 使用优化的 SIFT算法生成并降维图像特征描述子, 最后, 使用基于欧式距离和曼哈顿距离的优化双向匹配算法对图像特征进行匹配。仿真实验条件是通过计算机中仿真软件进行仿真实验, 统计图像特征数目、匹配数目、正确匹配数目、配准准确率、配准时间与配准时间下降率共 6个指标数据, 统计结果表明, CSS-SIFT算法在图像配准准确度方面与传统 SIFT算法、传统 SURF算法、Forstern-SIFT算法、Harris-SIFT算法、Trajkovic-SIFT算法相当, 但在图像配准耗时方面分别降低了 58.45%、10.68%、14.84%、16.21%与 4.63%, 为图像配准提供了一种解决方案。

To address the time-consuming problem of image registration in the scale-invariant feature transform(SIFT) algorithm, a curvature scale space (CSS)-SIFT composite image registration algorithm is proposed in this paper. First, the CSS-SIFT algorithm uses the CSS algorithm to extract image features. Image feature descriptors are then generated and reduced by the optimized SIFT algorithm. Finally, an optimized two-way matching algorithm based on Euclidean and Manhattan distances is used for matching.A simulation experiment is conducted using simulation software, and six parameters of index data are employed, including the number of image features, number of matches, number of correct matches, registration accuracy, registration time, and registration time decline rate. Statistical results show that the CSS-SIFT algorithm performs as well as the following algorithms in terms of accuracy of image registration: traditional SIFT, traditional speeded-up robust features, Forstern-SIFT, Harris-SIFT, and Trajkovic-SIFT. In addition, time-consumption of image registration is reduced by 58.45%, 10.68%, 14.84%, 16.21%, and 4.63%, respectively, thus providing an effective solution for image registration.