Abstract: Aiming at the problem that SIFT（Scale Invariant Feature Transform） algorithm extracts feature points that are impure and susceptible to noise, it is first proposed to preprocess the image before SIFT algorithm extracts feature points to eliminate some external interference. Secondly, for the 128-dimensional high-dimensional descriptor in SIFT algorithm, the matching speed is reduced. A feature descriptor based on the hierarchical region is proposed to reduce the descriptor sub-dimension and shorten the running time of the algorithm. Finally, aiming at the problem that the fixed threshold does not have wide applicability in the SIFT algorithm matching process, an adaptive threshold method is proposed to solve the problem that the fixed threshold does not match the actual image and improve the matching accuracy. The experimental results show that the improved algorithm can improve the matching accuracy and efficiency, enhance the robustness and reliability of the algorithm and has wide applicability.

Key words: image registration, Scale Invariant Feature Transform（SIFT）, adaptive threshold, RANSAC, hierarchical region

摘要： 针对SIFT（尺度不变特征变换）算法提取的特征点不纯、易受噪声等因素干扰的问题，提出在SIFT算法提取特征点之前对图像进行预处理，排除部分外界干扰。针对SIFT算法中128维的高维度特征描述符导致匹配速度降低，提出一种基于分层区域的方法降低描述符维度，缩短算法运行时间。针对SIFT算法匹配过程中选取固定阈值不具有广泛适用性的问题，提出一种自适应阈值的方法，解决设置固定阈值不能适用所有图像的问题，提高匹配准确率。实验结果证明，改进的算法能提高匹配准确率和匹配效率，增强算法的鲁棒性和可靠性，并且适用性广泛。

关键词: 图像配准, 尺度不变特征变换（SIFT）, 自适应阈值, 随机抽样一致（RANSAC）, 分层区域