基于SIFT算法的可见光宽带光谱图像配准方法研究

摘要

针对颜色与影像科学领域的可见光宽带光谱成像中存在的图像像素偏移问题，提出了基于SIFT算法的可见光宽带光谱图像配准方法。先利用SIFT算法提取图像特征点，再通过k-d树最近邻方法对特征点进行匹配，利用欧氏距离约束方法剔除错误匹配点，最后，利用均匀采样方法解决SIFT特征点容易聚集的问题，得到最优配准结果。在可见光宽带光谱图像配准实验中，基于均匀采样方法确定的最优配准结果与未经采样处理匹配点集的配准结果相比，配准之后的互信息值得到了显著提高。%To resolve the image shift problem of visible broadband spectral imaging in the field of color and imaging science, an improved image registration method based on Scale-invariant feature transforms (SIFT) algorithm is proposed. Firstly, the feature points are extracted by the SIFT algorithm. Secondly the feature points are matched by the nearest-neighbor rule of thek-d tree, and the euclidean distance is adopted to delete the false matching points. Finally, a uniform sampling method is applied to resolve the problem of SIFT feature points easy to gather and the optimal registration result is obtained. In the visible broadband spectral imaging registration test, the optimal registration result based on the uniform sampling method is compared to the result without sampling processing match points set, the former mutual information value is improved remarkably.