Unwrapping Phase Images: Theory and Applications Using Probabilistic Inference Techniques

展开相位图：使用概率推理技术的理论和应用

• 批准号: 0105719
• 负责人: Ralf Koetter
• 金额: $ 44.51万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-05-01 至 2005-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0105719&HistoricalAwards=false
• 关键词: Unwrapping; Phase; Images; Theory; Applications

Koetter AbstractPhase 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.

Koetter摘要二维拓扑中的相位展开是一个信号处理问题，在过去的20年里得到了广泛的研究，并有重要的应用，如医学成像和合成孔径雷达。 然而，尽管它在科学和工程中的重要性，到目前为止，在二维网格中的相位展开仍然是一个基本上未解决的问题。 这项研究采用了一种新的方法，从概率推理的方法来解决这个问题。 这项工作不仅有望产生强大的相位展开计划的基础上和产品算法和结构变分方法，但也有可能提供深入的理论洞察不适定的性质和可解性的相位展开问题。 这种见解对于指导实用算法的开发极其重要。本研究的主要目标是开发和改进通用、高效且显着改进早期方法的相位展开算法。在这方面的指导思想之一是使用概率推理作为相位展开方案中的非线性预处理步骤。 初步实验已经证实，传统技术的性能确实可以显着提高这种方法。 这项研究的成功可能会产生深远的实际影响。 例如，在SAR干涉测量中，相位展开是生成地形高程图的重要步骤，这项工作可以显著提高基于确定性相位模型的现有算法的精度。 同样，拟议的工作将使常规相位成像使用磁共振成像（MRI）信号可行，这将显着扩展MRI的临床实用性。