Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Sagittal collimating diaboloid: a new grazing-incidence mirror surface for higher-throughput resonant inelastic X-ray scattering spectrometers.

Journal of synchrotron radiation·2026
Same author

Choroid plexus remodeling linked to impaired CSF-mediated clearance and Alzheimer's disease progression.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Preoperative urge urinary incontinence and outcomes after thulium laser enucleation for benign prostatic hyperplasia.

World journal of urology·2026
Same author

Hybrid spectral-spatial domain registration for nanometric tracking in digital in-line holographic microscopy.

Optics letters·2026
Same author

Refractory Malignant Arrhythmia in a 4-Year-Old Child With Short QT Syndrome: Persistence for Hope.

JACC. Case reports·2026
Same author

Photorealistic 3D Holographic Display with Natural Defocus Effect.

Nature communications·2026
Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jan 1, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

Temporal phase unwrapping using deep learning.

Wei Yin1,2,3, Qian Chen4,5, Shijie Feng1,2,3

  • 1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing, Jiangsu Province, 210094, China.

Scientific Reports
|December 29, 2019
PubMed
Summary
This summary is machine-generated.

Deep learning-based temporal phase unwrapping (DL-TPU) reliably unwraps high-frequency phases, overcoming limitations of traditional multi-frequency methods. This machine learning approach enhances 3D imaging accuracy and speed in optical metrology.

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.4K
High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

16.0K

Related Experiment Videos

Last Updated: Jan 1, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.4K
High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

16.0K

Area of Science:

  • Optical Metrology
  • Computer Vision
  • Machine Learning

Background:

  • Multi-frequency temporal phase unwrapping (MF-TPU) is a classical algorithm for fringe projection techniques, enabling 3D reconstruction of complex surfaces.
  • MF-TPU's accuracy is limited by noise and error sources, typically restricting high-frequency patterns to about 16 fringes, thus limiting measurement precision.
  • Increasing fringe count for higher accuracy requires more patterns, prolonging measurement time.

Purpose of the Study:

  • To introduce and validate a deep learning-based temporal phase unwrapping (DL-TPU) method for enhanced phase unwrapping reliability.
  • To demonstrate DL-TPU's capability to handle various error sources common in optical measurements.
  • To achieve reliable unwrapping of high-frequency phases (e.g., 64 periods) directly from low-frequency patterns.

Main Methods:

  • Supervised learning was employed to train deep learning models for automatic temporal phase unwrapping (TPU).
  • The DL-TPU method was compared against the traditional MF-TPU approach under various error conditions.
  • Experimental validation was performed to demonstrate the practical application and performance of DL-TPU.

Main Results:

  • DL-TPU significantly improved unwrapping reliability compared to MF-TPU, effectively mitigating issues like intensity noise, low fringe modulation, projector nonlinearity, and motion artifacts.
  • The study experimentally demonstrated the direct and reliable unwrapping of high-frequency phases (64 periods) from a unit-frequency phase using DL-TPU.
  • This represents a substantial advancement over MF-TPU's limitations in achievable fringe frequency and accuracy.

Conclusions:

  • Deep learning offers a powerful solution to overcome inherent challenges in optical metrology, particularly in phase unwrapping.
  • DL-TPU enables higher measurement accuracy and sensitivity by reliably processing high-frequency fringe patterns.
  • The findings pave the way for developing advanced, high-speed, and highly accurate 3D imaging systems for diverse scientific and industrial applications.