Modified phase correlation algorithm for image registration based on pyramid

Abstract

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.

Keywords

• Panoramic target detection system
• Image registration
• Pyramid phase correlation algorithm
• Deep learning