Estimation of Displacement Vector Field from Noisy Data using Maximum Likelihood Estimator

The present study proposes an approach for robust motion estimation between two successive image frames, from a degraded sequence. The method is based on generalized cross-correlation (GCC) methods, where the phase of the Fourier components is used for motion parameter estimation. This method uses "whitening" FIR filters to sharpen the cross-correlation maximum, thereby improving the accuracy of identification of the peak. The estimators of interest are the phase transform (PHAT), and the maximum likelihood (ML) estimators. For robust motion estimation it has been found that the ML estimator is particularly suited to this purpose. The accuracy of the estimators is also discussed. Significant results have been obtained for sub-pixel translation of images of different nature and across different spectral bands.