Li Hao, Wang Lipo, Zhao Tianyun, Zhao Wei
State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwest University, Xi'an 710127, China.
UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai 200030, China.
Sensors (Basel). 2024 Sep 4;24(17):5759. doi: 10.3390/s24175759.
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 are thus computationally expensive, 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. Benefiting from the adjustable size of the interrogation window, the LP-SIFT algorithm demonstrates comparable or even less stitching time than the other commonly used algorithms, while achieving comparable or even better stitching results. 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.
图像拼接旨在构建具有高空间分辨率的宽视野，这在单次曝光中是无法实现的。通常，除深度学习外的传统图像拼接技术需要复杂的计算，因此计算成本高昂，尤其是在拼接大型原始图像时。在本研究中，受流体湍流多尺度特征的启发，我们基于多尺度局部峰值和尺度不变特征变换方法，开发了一种名为局部峰值尺度不变特征变换（LP-SIFT）的快速特征点检测算法。通过在图像拼接中结合LP-SIFT和RANSAC，与原始SIFT方法相比，拼接速度可提高几个数量级。得益于询问窗口大小可调，LP-SIFT算法在拼接时间上与其他常用算法相当甚至更短，同时实现了相当甚至更好的拼接效果。九张随机排列且无先验知识的大型图像（超过2600×1600像素）可在158.94秒内完成拼接。该算法对于在各种应用场景中需要宽视野的应用非常实用，例如地形测绘、生物分析，甚至刑事调查。
