Multiframe Shift Estimation

The purpose of this research was to develop a fundamental framework for a new approach to multiframe translational shift estimation in image processing. This thesis sought to create a new multiframe shift estimator, to theoretically prove and experimentally test key properties of it, and to quantify its performance according to several metrics. The new estimator was modeled successfully and was proven to be an unbiased estimator under certain common image noise conditions. Furthermore its performance was shown to be superior to the cross correlation shift estimator, a robust estimator widely used in similar image processing cases, according to several criteria. This research effort led to the derivation of a lower bound of estimation performance for the multiframe case. This valuable data analysis tool extends current boundary derivations to include prior information about the random shifting, thereby providing a more precise performance boundary.