Moran, Jodi2006-08-222006-08-2220012001http://hdl.handle.net/10012/1107The reconstruction of a phase surface from the observed principal values is required for a number of applications, including synthetic aperture radar (SAR) and magnetic resonance imaging (MRI). However, the process of reconstruction, called “phase unwrapping”, is an ill-posed problem. One class of phase-unwrapping algorithms uses smoothness prior models to remedy this situation. We categorize this class of algorithms according to the type of prior model used. Motivated by this categorization, we propose that phase-unwrapping algorithms be tested by generating phase surfaces from the prior models, and then quantifying the deviation of each reconstructed surface from the corresponding original surface. Finally, we present results of the new testing method on a selection of phase-unwrapping algorithms, including a new algorithm.application/pdf7824141 bytesapplication/pdfenCopyright: 2001, Moran, Jodi. All rights reserved.Computer Sciencephase unwrappingbayesianprobabilitytestingMaster Thesis