Particle-filter-based phase estimation in digital holographic interferometry

Abstract

In this paper, we propose a particle-filter-based technique for the analysis of a reconstructed interference field. The particle filter and its variants are well proven as tracking filters in non-Gaussian and nonlinear situations. We propose to apply the particle filter for direct estimation of phase and its derivatives from digital holographic interferometric fringes via a signal-tracking approach on a Taylor series expanded state model and a polar-to-Cartesian-conversion-based measurement model. Computation of sample weights through non-Gaussian likelihood forms the major contribution of the proposed particle-filter-based approach compared to the existing unscented-Kalman-filter-based approach. It is observed that the proposed approach is highly robust to noise and outperforms the state-of-the-art especially at very low signal-to-noise ratios (i.e., especially in the range of -5 to 20 dB). The proposed approach, to the best of our knowledge, is the only method available for phase estimation from severely noisy fringe patterns even when the underlying phase pattern is rapidly varying and has a larger dynamic range. Simulation results and experimental data demonstrate the fact that the proposed approach is a better choice for direct phase estimation.

Comparative study of phase unwrapping algorithms based on solving the Poisson equation

“…Overall, the iterative methods, especially the TIE-FD-DCT-based method, are recommended to be used in the actual applications. However, the performance of the iterative methods was not so satisfactory under a very heavy noise condition (as shown in figures 8(c6)-(d6)), a weighting [81][82][83] or pre-filtering [37,[84][85][86][87][88][89] strategy can be adopted to further improve the performance of those methods. Besides, with a booming development of deep learning [90][91][92][93][94][95][96][97], using a proper network to enhance or accomplish the phase unwrapping is also a promising research direction.…”

Spatial Relation Awareness Module for Phase Unwrapping

“…Phase unwrapping is used to measure physical quantities, such as variations and surface shapes, in various practical fields, including magnetic resonance imaging [1], synthetic aperture radar [2], fringe projection techniques [3], and digital holographic interferometry [4]. In general, the phase measurement is typically acquired using the arctangent function, which is limited to the range of (−π, π].…”

C-HRNet: High Resolution Network Based on Contexts for Single-Frame Phase Unwrapping

“…hase is directly proportional to the shape of objects,terrain elevation,and magnetic field nonuniformity in applications such as fringe projection profilometry (FPP) [1], [3],magnetic resonance imaging (MRI) [2],synthetic aperture radar (SAR) [4], [5],digital holographic interferometry (DHI) [6],and wave-front compensation [7], [8].However,in these applications,the calculated phase information is often wrapped within the range of -π to π ,and it needs to be unwrapped to obtain continuous phase.This process is known as phase unwrapping.To eliminate jumps and obtain continuous phase,various methods have been proposed,which can be categorized into temporal phase unwrapping and spatial phase Wenbo Zhao is with the School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China(e-mail: 21082304141@mails.guet.edu.cn).…”