Efficient subpixel image registration algorithm for high precision visual vibrometry

As one of the most reliable motion estimation algorithms that apply phase correlation methods, the single step DFT (SSDFT) approach has superior characteristics, including the high accuracy and low complexity. However, this approach is limited by the accuracy of the initial estimation. Therefore, it is an enormous challenge to reduce the dimension of the searching area in the subsequent refinement step. As a result, this algorithm is inefficient with large upsampling factors. In order to overcome this problem, an improved twostep image registration algorithm is proposed in the present study. In the first step, the accuracy of the initial estimation is improved by using a motion amplified cross-correlation function. The improved initial estimation is then amended to ensure that retained error is eliminated. In the refinement step, the dimension of the searching area is effectively reduced in accordance with the improved initial estimation and upsampling factor. Obtained results show that for large upsampling factors, the modified SSDFT achieves the same subpixel accuracy as the original algorithm. Meanwhile, it is found that the modified scheme remarkably reduces the computational expense. Finally, conducted experiments on high-speed video sequences demonstrate that the proposed modifications significantly reduce the required time for high precision target tracking tasks.