Image exploitation algorithms often require image registration to align imagery prior to processing. For example, for image fusion processing, image alignment enables consistent extraction of corresponding features. For geo-location applications, mission imagery, acquired by an onboard camera, properly-aligned with geo-located reference imagery enables extraction of geo-coordinates from pixels-of-interest. Geo-coordinates of candidate target or landmark pixels result from association with geo-located reference pixels via the image alignment. Image correlation represents a standard, classic form of image matching and image registration, which has been adapted and modified in several ways over the years. One modification consists of applying alpha-rooting to the Fourier domain image magnitudes to implement tunable Fourier domain image whitening. The whitening process emphasizes the phase content, which contains much of the image information, and mitigates effects from amplitude variations due to changing illumination conditions. Choice of alpha-rooting parameters provides the capability to adapt the whitening characteristics to the properties of the imagery, and the application in use. In this paper, we present an iterative image registration algorithm that exploits the shape properties of a region of the peak correlation coefficient, under progressive alpha-rooting parameter sharpening. Together with a coarse grid search approach, we converge to the correct image registration solution, while reducing computational load relative to a high resolution parameter space search. We provide a brief review of alpha-rooted phase correlation and describe the technical formulation. We present numerical results showing the effectiveness of the approach relative to standard correlation approaches.
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