Deep convolutional neural network phase unwrapping for fringe projection 3d imaging

Jian Liang, Junchao Zhang, Jianbo Shao, Bofan Song, Baoli Yao, Rongguang Liang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.

Original languageEnglish (US)
Article number3691
Pages (from-to)1-11
Number of pages11
JournalSensors (Switzerland)
Issue number13
StatePublished - Jul 2020


  • 3D imaging
  • Deep learning
  • Phase unwrapping

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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