research-article

A Deep Learning-based Model for Phase Unwrapping

Authors:

G. E. Spoorthi,

Subrahmanyam Gorthi,

Rama Krishna Sai GorthiAuthors Info & Claims

ICVGIP '18: Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing

Article No.: 19, Pages 1 - 8

https://doi.org/10.1145/3293353.3293372

Published: 03 May 2020 Publication History

Abstract

Phase unwrapping is an important problem in several applications that attempts to restore original phase from wrapped phase. In this paper, we propose a novel phase unwrapping model based on the deep convolutional neural network by formulating the phase unwrapping as a semantic segmentation problem. The proposed architecture consists of a convolutional encoder network and corresponding decoder network followed by a pixel-wise classification layer. One of the critical challenges in DCNN is availability of large set of labeled training data. This issue is effectively circumvented for the proposed framework through a generic simulation procedure that automatically generates large labeled data. Results from the proposed method are compared with widely used quality-guided phase unwrapping algorithm for various SNR values. It is found that the proposed method is performing well both in terms of accuracy and computational time, even in the presence strong noise. To the best of our knowledge, this is the first work that uses convolutional neural network for phase unwrapping, and this will hopefully pave the way to a new class of techniques for unwrapping the phase.

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Cited By

Wang XJu CXuan YShi TYang FLiu YKou KZhao Y(2024)Improved Res-UNet Network for Phase Unwrapping of Interferometric Gear Tooth Flank MeasurementsPhotonics10.3390/photonics1107067111:7(671)Online publication date: 18-Jul-2024
https://doi.org/10.3390/photonics11070671
Kuang YLiu FLiu YChen XWu YZhang R(2024)Correction of spurious phase sign in single closed-fringe demodulation using transformer based Swin-ResUnetOptics & Laser Technology10.1016/j.optlastec.2023.109952168(109952)Online publication date: Jan-2024
https://doi.org/10.1016/j.optlastec.2023.109952
Kuang YLi JLiu FWu YZhang R(2023)Fast and high precision phase recovery technology of single-shot ineterferogram based on depth convolution neural networkJournal of Optics10.1088/2040-8986/ad158926:2(025701)Online publication date: 22-Dec-2023
https://doi.org/10.1088/2040-8986/ad1589
Show More Cited By

Index Terms

A Deep Learning-based Model for Phase Unwrapping
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Reconstruction
  2. Computer graphics
    1. Image manipulation
      1. Image processing
2. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems
      1. Digital signal processing

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ICVGIP '18: Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing

December 2018

659 pages

ISBN:9781450366151

DOI:10.1145/3293353

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 03 May 2020

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ICVGIP 2018

ICVGIP 2018: 11th Indian Conference on Computer Vision, Graphics and Image Processing

December 18 - 22, 2018

Hyderabad, India

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Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

Wang XJu CXuan YShi TYang FLiu YKou KZhao Y(2024)Improved Res-UNet Network for Phase Unwrapping of Interferometric Gear Tooth Flank MeasurementsPhotonics10.3390/photonics1107067111:7(671)Online publication date: 18-Jul-2024
https://doi.org/10.3390/photonics11070671
Kuang YLiu FLiu YChen XWu YZhang R(2024)Correction of spurious phase sign in single closed-fringe demodulation using transformer based Swin-ResUnetOptics & Laser Technology10.1016/j.optlastec.2023.109952168(109952)Online publication date: Jan-2024
https://doi.org/10.1016/j.optlastec.2023.109952
Kuang YLi JLiu FWu YZhang R(2023)Fast and high precision phase recovery technology of single-shot ineterferogram based on depth convolution neural networkJournal of Optics10.1088/2040-8986/ad158926:2(025701)Online publication date: 22-Dec-2023
https://doi.org/10.1088/2040-8986/ad1589
Briñez-de León JRico-García MRestrepo-Martínez A(2022)PhotoelastNet: a deep convolutional neural network for evaluating the stress field by using a single color photoelasticity imageApplied Optics10.1364/AO.44456361:7(D50)Online publication date: 10-Feb-2022
https://doi.org/10.1364/AO.444563
Kar JCohen MMcQuiston SPoorsala TMalozzi C(2022)Direct left-ventricular global longitudinal strain (GLS) computation with a fully convolutional networkJournal of Biomechanics10.1016/j.jbiomech.2021.110878130(110878)Online publication date: Jan-2022
https://doi.org/10.1016/j.jbiomech.2021.110878

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