Unwrapping Phase Images: Theory and Applications Using Probabilistic Inference Techniques

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

Koetter Abstract Phase unwrapping in 2-dimensional topologies is a signal-processing problem that has been extensively studied over the past 20 years and has important applications, such as medical imaging and synthetic aperture radar. However, despite its importance in science and engineering, to date, phase unwrapping in 2-dimensional grids has remained an essentially unsolved problem. This research takes a fresh approach to the problem using methods from probabilistic inference. The work not only holds the promise of resulting in powerful phase unwrapping schemes based on the sum-product algorithm and structured variational methods, but also has the potential to provide deep theoretical insight into the ill-posed nature and solvability of the phase unwrapping problem. Such an insight is extremely important for guiding the development of practical algorithms. The main objective of this research is to develop and refine algorithms for phase unwrapping that are versatile, efficient and that significantly improve upon earlier approaches. One of the guiding ideas in this context is the use of probability inference as a nonlinear preprocessing step in a phase unwrapping scheme. Initial experiments have confirmed that the performance of traditional techniques can indeed be significantly boosted with such an approach. Success of this research can have a profound practical impact. For example, in SAR interferometry, phase unwrapping is an essential step in generating terrain elevation maps, and this work can significantly enhance the accuracy of existing algorithms based on deterministic phase models. Similarly, the proposed work will make routine phase imaging using Magnetic Resonance Imaging (MRI) signals feasible, which will significantly extend the clinical utility of MRI.