Image stitching aims to construct a wide field of view with high spatial
resolution, which cannot be achieved in a single exposure. Typically,
conventional image stitching techniques, other than deep learning, require
complex computation and thus computational pricy, especially for stitching
large raw images. In this study, inspired by the multiscale feature of fluid
turbulence, we developed a fast feature point detection algorithm named
local-peak scale-invariant feature transform (LP-SIFT), based on the multiscale
local peaks and scale-invariant feature transform method. By combining LP-SIFT
and RANSAC in image stitching, the stitching speed can be improved by orders,
compared with the original SIFT method. Nine large images (over 2600*1600
pixels), arranged randomly without prior knowledge, can be stitched within
158.94 s. The algorithm is highly practical for applications requiring a wide
field of view in diverse application scenes, e.g., terrain mapping, biological
analysis, and even criminal investigation.

通过结合局部极值尺度不变特征变换（LP-SIFT）算法和 RANSAC 算法，在图像拼接中实现了极大的速度提升，可以在 158.94 秒内拼接九张 2600*1600 像素的大图片，并且适用于地形绘制、生物分析和刑事调查等多种领域。