基于拉普拉斯金字塔融合的岩心图像拼接算法

针对岩心图像拼接效率低以及易出现鬼影现象的问题,提出了一种基于最佳缝合线的拉普拉斯金字塔融合的岩心图像拼接方法.首先将待拼接的两幅岩心图像进行灰度变换,根据ORB算法计算并描述特征点;其次使用改进的random sample consensus (RANSAC)算法对特征点进行提纯,完成特征点匹配;根据匹配的特征点计算图像间的配准关系,最后根据最佳缝合线实现岩心图像的拉普拉斯金字塔融合,完成拼接.实验结果表明,改进的RANSAC算法能在保证正确率的同时提升速度,而且本文提出的图像融合方法避免了鬼影的产生,在融合区域的PSNR、SSIM和DoEM客观评价指标上与另外两种图像融合算法相比都有所提升.

Core Image Stitching Algorithm Based on Laplacian Pyramid Fusion

A core image stitching method based on Laplacian pyramid fusion with the best seam-line is proposed to address the problems of low core image stitching efficiency and the tendency of ghosting. Firstly, two core images to be stitched are processed through grey-level transformation, and then feature points are calculated and described according to the ORB algorithm. Secondly, the improved random sample consensus (RANSAC) algorithm is used to purify the feature points and complete feature point matching. According to the matched feature points, the alignment relationship between the images is calculated. Finally, the Laplacian pyramid fusion of the core images is realized based on the best seam-line, and the stitching is completed. The experimental results show that the improved RANSAC algorithm can improve the speed while ensuring accuracy, and the proposed image fusion method avoids the generation of ghosting and performs better on the PSNR, SSIM, and DoEM objective evaluation indexes of the fusion region compared with the other two image fusion algorithms.