Image registration is an important process for applications in various fields, such as remote sensing and medical imaging; thus, its accuracy significantly affects the efficacy as well as efficiency of those applications. Phase correlation algorithm (PCA) and normalized cross correlation-pyramid (NCCP) algorithm are the state-of-the-art frequency domain and spatial domain methods for image registration, respectively. However, these algorithms have some limitations. In particular, the registration speed of PCA needs to be improved, while the NCCP algorithm leads to errors if the image to be registered is partially occluded. Thus, to overcome these limitations, we propose a pyramid PCA that combines both algorithms. To verify the performance of our proposed algorithm, its results are compared with those obtained using the traditional PCA and NCCP algorithm. Our simulation results for partially occluded images indicate that the proposed algorithm outperforms the NCCP algorithm in terms of accuracy; in addition, it outperforms PCA in terms of speed. Furthermore, to test the feasibility of the proposed algorithm for real-time applications, a panoramic target detection system was set up, and the results obtained using the system proved that our method for image registration was both feasible and effective.

图像配准是遥感和医学成像等各个领域应用的重要过程；因此，其准确性会显着影响这些应用程序的功效和效率。相位相关算法 (PCA) 和归一化互相关金字塔 (NCCP) 算法分别是最先进的频域和空间域图像配准方法。然而，这些算法有一些局限性。特别是PCA的配准速度有待提高，而NCCP算法如果要配准的图像被部分遮挡会导致错误。因此，为了克服这些限制，我们提出了一种结合了两种算法的金字塔 PCA。为了验证我们提出的算法的性能，将其结果与使用传统 PCA 和 NCCP 算法获得的结果进行比较。我们对部分遮挡图像的模拟结果表明，所提出的算法在准确性方面优于 NCCP 算法；此外，它在速度方面优于 PCA。此外，为了测试所提算法在实时应用中的可行性，建立了全景目标检测系统，使用该系统获得的结果证明我们的图像配准方法既可行又有效。