摘要： 针对大视差图像拼接后重叠区域出现重影、非重叠区域发生透视失真等问题，提出一种改进的大视差图像拼接算法。利用尽可能投影算法（APAP）建立低密度网格形变，根据待拼接图像成对匹配点的分布对重叠区域内的网格形变进行细分。通过随机采样一致性算法计算全局最优相似矩阵，校正非重叠区域发生的透视失真现象。将全局最优相似矩阵与网格单应矩阵加权叠加，实现目标图像形变。在此基础上，对目标图像重叠区域进行内容感知，保留重要度较低的区域并完成拼接，以避免重叠区域出现重影问题。实验结果表明，相对APAP、SPHP等算法，该算法的拼接效果更能还原真实场景，且拼接图像的均方根误差值较低。

关键词: 大视差图像拼接, 网格形变, 全局最优相似矩阵, 内容感知, 加权叠加

Abstract: The stitching of large parallax images can cause double image in overlapping area and perspective distortion in non-overlapping area.To address these problems,this paper proposes an improved large parallax image stitching algorithm.The low density mesh deformation is established by the As-Projective-As-Possible(APAP) algorithm,and the mesh deformation in the overlapping area is subdivided according to the distribution of paired matching points of images to be stitched.The global optimal similar matrix is calculated by random sample consensus algorithm,and the perspective distortion in non-overlapping area is corrected.Then the global optimal similar matrix and the mesh homography matrix are weighted and superposed,so as to realize the deformation of the target image.On this basis,content awareness is executed in the overlapping area of the target image,in which the area of less importance is retained and spliced,thus avoiding the double image problem in the overlapping area.Experimental results show that compared with other algorithms,such as the APAP algorithm and the SPHP algorithm,the proposed algorithm can better restore the real scenario,and with this algorithm,the root mean square error of the stitched image is lower.

Key words: large parallax image stitching, mesh deformation, global optimal similar matrix, content awareness, weighted overlay

中图分类号: 

• TP391