UAV Image Stitching via Global Optimal Seamline Detection and Local Alignment With Seamline Constraint

The goal of image stitching is to generate high-quality panoramic images with minimal computational cost. However, variations in viewpoint or scene depth can cause parallax effects in UAV images, complicating precise alignment and leading to artifacts such as ghosting, blurring, and misalignment. While advanced seamline detection algorithms reduce ghosting and blurring, structural distortions and misalignments near the seamline often remain, negatively affecting stitching quality. Moreover, these algorithms typically face challenges in balancing computational efficiency with alignment accuracy. In this paper, we propose a robust and flexible UAV image stitching method based on global optimal seamline detection and local alignment with seamline constraint. Our approach ensures precise alignment while maintaining processing efficiency. First, a global transformation-based alignment algorithm is used to pre-align the images to a common coordinate system. Then, an efficient weighted fast sweeping (WFS) algorithm is proposed to detect the globally optimal seamline, minimizing artifacts in overlapping regions caused by alignment errors and dynamic objects. Finally, an optical flow-guided local alignment method with seamline constraint is developed to correct residual misalignments along the seamline, reducing global structural distortion. Extensive experiments on a range of challenging datasets demonstrate that the proposed method outperforms existing approaches, producing more natural-looking stitching results.