Deep learning approaches for unwrapping phase images with steep spatial gradients: A simulation

Gili Dardikman, Nir A. Turko, Natan T. Shaked

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We explore different deep learning-based approaches to solve the problem of phase unwrapping in objects with high spatial gradients, which is applicable to many fields in medicine, biology and remote sensing. We simulate data with high spatial gradients to compare the quality of the solution and the runtime obtained when addressing this problem either as an inverse problem or as a semantic segmentation problem.

Original languageEnglish
Title of host publication2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538663783
DOIs
StatePublished - 20 Feb 2019
Event2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 - Eilat, Israel
Duration: 12 Dec 201814 Dec 2018

Publication series

Name2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018

Conference

Conference2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
Country/TerritoryIsrael
CityEilat
Period12/12/1814/12/18

Keywords

  • Deep learning
  • inverse problems
  • phase imaging
  • phase unwrapping
  • semantic segmentation

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