Robust subpixel shift estimation using iterative phase correlation of a local region

In this paper, subpixel shift estimation method using phase correlation with local region is proposed for registration of noisy images. Commonly, phase correlation based on the Fourier shift property is used to estimate the shift between images. Subpixel shift of images can be estimated by the analysis for the phase correlation of downsampled images. However, in case of images with noise or aliasing artifacts, the error in estimation is increased. Thus, we consider a small region in a corner of an image instead of the whole, because flat regions with noise and regions with aliasing induce the error of estimation. In addition, to improve accuracy, the local regions are inversely shifted by varying the subpixel shift values, and obtaining the peak value of phase correlation between the images. Then, the subpixel shift value corresponding to the maximum of the peak values is selected. Real-time implementation of this process is possible because only a local region is used, thereby reducing the process time. In experiments, the proposed method is compared with conventional methods using several fitting functions, and it is applied for the task of super resolution imaging. The proposed method shows higher accuracy in registration than other methods, also, edge-sharpness in superresolved images is improved.