One-step robust deep learning phase unwrapping

Kaiqiang Wang, Ying Li, Qian Kemao, Jianglei Di, Jianlin Zhao

Research output: Contribution to journalArticlepeer-review

335 Scopus citations

Abstract

Phase unwrapping is an important but challenging issue in phase measurement. Even with the research efforts of a few decades, unfortunately, the problem remains not well solved, especially when heavy noise and aliasing (undersampling) are present. We propose a database generation method for phase-type objects and a one-step deep learning phase unwrapping method. With a trained deep neural network, the unseen phase fields of living mouse osteoblasts and dynamic candle flame are successfully unwrapped, demonstrating that the complicated nonlinear phase unwrapping task can be directly fulfilled in one step by a single deep neural network. Excellent anti-noise and anti-aliasing performances outperforming classical methods are highlighted in this paper.

Original languageEnglish
Pages (from-to)15100-15115
Number of pages16
JournalOptics Express
Volume27
Issue number10
DOIs
StatePublished - 2019

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