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基于无迹卡尔曼滤波器、增强型相位梯度估计器和路径跟踪策略的增强型相位展开算法。

Enhanced phase unwrapping algorithm based on unscented Kalman filter, enhanced phase gradient estimator, and path-following strategy.

作者信息

Xie XianMing, Li YingHui

出版信息

Appl Opt. 2014 Jun 20;53(18):4049-60. doi: 10.1364/AO.53.004049.

Abstract

This paper presents an enhanced phase unwrapping algorithm by combining an unscented Kalman filter, an enhanced local phase gradient estimator based on an amended matrix pencil model, and a path-following strategy. This technology is able to accurately unwrap seriously noisy wrapped phase images by applying the unscented Kalman filter to simultaneously perform noise suppression and phase unwrapping along the path from the high-quality region to the low-quality region of the wrapped phase images. Results obtained with synthetic data and real data validate the effectiveness of the proposed method and show improved performance of this new algorithm with respect to some of the most used algorithms.

摘要

本文提出了一种增强型相位展开算法,该算法结合了无迹卡尔曼滤波器、基于修正矩阵束模型的增强型局部相位梯度估计器和路径跟踪策略。通过应用无迹卡尔曼滤波器,沿着包裹相位图像从高质量区域到低质量区域的路径同时进行噪声抑制和相位展开,该技术能够准确地对噪声严重的包裹相位图像进行相位展开。使用合成数据和真实数据获得的结果验证了所提方法的有效性,并表明该新算法相对于一些最常用算法具有更好的性能。

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