Bayesian MRF Modeling and Graph Cuts for Phase Unwrapping with Discontinuity Phase Flaws：A Comparative Study

Abstract

Phase unwrapping (PU) is a difficult task commonly found in applications involving interferometric synthetic aperture radar (InSAR), magnetic resonance imaging (MRI) and optical surface profile measurements; all of which involve mathematically ill-posed problems. Conventional algorithms exhibit strong shortcomings in PU when phase discontinuity flaws exist. To simulate these situations, we are custom-designed test data with a phase discontinuity flaw. This simulated data is a 3D Gaussian distribution with an arc-shaped notch as a phase flaw. PU is carried out by Bayesian inference and MRF (Markov Random Field) modeling. A graph cut algorithm is employed for optimization with respect to energy minimization. Three other conventional algorithms are also employed and their PU performance is compared. The results show the good performance and effectiveness of the Bayesian MRF modeling method. These experimental results are important references for phase unwrapping problems when phase discontinuities exist.