Image Registration With Fourier-Based Image Correlation: A Comprehensive Review of Developments and Applications

Abstract

Fourier-based image correlation is a powerful area-based image registration technique, which involves aligning images based on a translation model or similarity model by means of the image information and operation in the frequency domain. In recent years, Fourier-based image correlation has made significant progress and attracted extensive research interest in a variety of applications, especially in the field of photogrammetry and remote sensing, leading to the development of a number of subpixel methods that have improved the accuracy and robustness. However, to date, a detailed review of the literature related to Fourier-based image correlation is still lacking. In this review, we aim at providing a comprehensive overview of the fundamentals, developments, and applications of image registration with Fourier-based image correlation methods. Specifically, this review introduces the principal laws underlying these methods, presents a survey of the existing subpixel methods calculated both in the spatial domain and in the frequency domain, summarizes the major applications from three aspects, and discusses the challenges and possible directions of future research. This review is expected to be beneficial for researchers working in the relevant fields to obtain an insight into the current state of the art, to develop new variants, to explore potential applications, and to suggest promising future trends of image registration with Fourier-based image correlation.