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.