A windowed phase correlation algorithm for subpixel motion estimation

This paper presents a windowed phase correlation algorithm for subpixel motion estimation. Motion estimation methods are crucial for video coding, image stabilization, image deblurring, and micro-mechanical motion compensation, etc. Conventional phase-based correlation algorithms usually have reduced precision or bias error due to aliasing and edge effects in real sampled imaging system. A window function is applied to images in the spatial domain before Fourier transformation to suppress frequency leakage. Further more, unreliable frequencies due to aliasing errors are masked out in the frequency domain. Experiments show that the proposed approach yields improved accuracy and superior precision for motion estimation in the presence of aliasing and edge effects in real image systems compared to conventional phase-based correlation algorithms.