Exploiting the quasi-linear relationship between local phase and disparity, phase-differencing registration algorithms provide a fast, powerful means for disparity estimation. Unfortunately, phase-differencing techniques suffer from a significant impediment: the neglect of multi- scale information. In this work, we introduce a novel registration algorithm that combines strategies of both phase- differencing and local correlation. This hybrid approach retains the advantageous properties of phase-differencing while incorporating the multiscale aspects of local correlation.