A Bispectrum Technique to Subpixel Image Registration under Noisy Conditions

This paper proposes an effective higher-order statistics method to address subpixel image registration. Conventional power spectrum-based techniques employ second-order statistics to estimate subpixel translation between two images. They are, however, susceptible to noise, thereby leading to significant performance deterioration in low signal-to-noise (SNR) environments. In view of this, we propose a bispectrum-based approach to alleviate this difficulty. The new method utilizes the characteristics of bispectrum to suppress Gaussian noise. It develops the phase relationship between the image pair, and estimates the subpixel translation by solving a set of nonlinear equations derived from the bispectrum. Experimental results show that the proposed method is effective in identifying subpixel translations under different noise levels and environments