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 Experiment Videos

Phase local approximation (PhaseLa) technique for phase unwrap from noisy data.

Vladimir Katkovnik1, Jaakko Astola, Karen Egiazarian

  • 1Signal Processing Institute, University of Technology of Tampere, Tampere, Finland. katkov@cs.tut.fi

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|May 17, 2008
PubMed
Summary
This summary is machine-generated.

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

DM-CFO: A Diffusion Model for Compositional 3D Tooth Generation With Collision-Free Optimization.

IEEE transactions on visualization and computer graphics·2026
Same author

Roadmap on computational methods in optical imaging and holography [invited].

Applied physics. B, Lasers and optics·2024
Same author

Miniature color camera via flat hybrid meta-optics.

Science advances·2023
Same author

Hybrid diffractive optics design via hardware-in-the-loop methodology for achromatic extended-depth-of-field imaging.

Optics express·2022
Same author

Power-balanced hybrid optics boosted design for achromatic extended depth-of-field imaging via optimized mixed OTF.

Applied optics·2021
Same author

Polarization holographic recording of vortex diffractive optical elements on azopolymer thin films and 3D analysis via phase-shifting digital holographic microscopy.

Optics express·2021
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Local polynomial approximation (LPA) reconstructs absolute phase from noisy wrapped data. An adaptive window selection method improves accuracy, outperforming existing phase unwrapping algorithms.

Area of Science:

  • Signal Processing
  • Nonparametric Statistics

Background:

  • Wrapped phase data is common in scientific measurements.
  • Estimating absolute phase from noisy wrapped data is challenging.
  • Existing phase unwrapping algorithms have limitations in accuracy and handling large phase differences.

Purpose of the Study:

  • To develop a novel method for absolute phase estimation from noisy wrapped phase data.
  • To improve the accuracy of phase reconstruction compared to state-of-the-art algorithms.
  • To enable phase unwrapping beyond the principal interval [-pi, pi].

Main Methods:

  • Application of local polynomial approximation (LPA) to estimate the argument of trigonometric functions.
  • Adaptive window size selection using the intersection of confidence interval (HCI) algorithm.

Related Experiment Videos

  • Gauss-Newton recursive procedure for efficient computation and tracking.

    • Main Results:

    • The LPA-based method achieves high accuracy in absolute phase estimation.
    • The adaptive window selection leads to near-optimal mean squared error.
    • The algorithm successfully reconstructs absolute phase even with large phase differences and noise.
    • Performance surpasses existing methods for noisy phase unwrapping.

    Conclusions:

    • The proposed LPA and HCI-based algorithm offers a robust and accurate solution for absolute phase estimation.
    • The method provides a significant advancement in noisy phase unwrapping techniques.
    • Theoretical analysis supports the effectiveness of the HCI adaptation for pointwise accuracy.