基于网格运动统计算法和最佳缝合线的密集重复结构图像快速拼接方法

针对常用的图像拼接算法对具有密集重复结构的图像会产生大量误匹配点从而出现明显鬼影且耗时较长的问题，将网格运动统计（GMS）算法与最佳缝合线算法相结合，提出了一种密集重复结构的图像快速拼接方法。首先，在图像的重叠区域提取大量粗匹配点；接着，采用GMS算法进行精匹配，然后在此基础上估计变换模型；最后，采用基于动态规划思想的最佳缝合线算法完成图像拼接。实验结果表明，将所提算法应用于两组具有密集重复结构的图像上，不仅可以有效消除鬼影，得到理想的拼接效果，而且显著减少了拼接时间；平均拼接速度分别是传统尺度不变特征变换（SIFT）和加速稳健特征（SURF）算法的7.4倍和3.2倍，分别是结合区域分块的SIFT算法和SURF算法的4.1倍和1.4倍。所提算法能够有效地消除密集重复结构拼接时的鬼影，同时缩短了拼接时间。

Fast stitching method for dense repetitive structure images based on grid-based motion statistics algorithm and optimal seam

For the images with dense repetitive structure, the common algorithms will lead to a large number of false matches, resulting in obvious ghosting in final image and high time consumption. To solve the above problems, a fast stitching method for dense repetitive structure images was proposed based on Grid-based Motion Statistics (GMS) algorithm and optimal seam algorithm. Firstly, a large number of coarse matching points were extracted from the overlapping regions. Then, the GMS algorithm was used for precise matching, and the transformation model was estimated based on the above. Finally, the dynamic-programming-based optimal seam algorithm was adopted to complete the image stitching. The experimental results show that, the proposed method can effectively stitch images with dense repetitive structures. Not only ghosting is effectively suppressed, but also the stitching time is significantly reduced, the average stitching speed is 7.4 times and 3.2 times of the traditional Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) algorithms respectively, 4.1 times as fast as the area-blocking-based SIFT algorithm, 1.4 times as fast as the area-blocking-based SURF algorithm. The proposed algorithm can effectively eliminate the ghosting of dense repetitive structure splicing and shorten the stitching time.