DeepDensity: Convolutional neural network based estimation of local fringe pattern density-Reference-Cited by-同舟云学术

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DeepDensity: Convolutional neural network based estimation of local fringe pattern density

Published:2021-10 Issue: Volume:145 Page:106675
ISSN:0143-8166
Container-title:Optics and Lasers in Engineering
language:en
Short-container-title:Optics and Lasers in Engineering

Author:

Cywińska Maria^ORCID,Brzeski Filip^ORCID,Krajnik Wiktor,Patorski Krzysztof,Zuo Chao,Trusiak Maciej

Funder

Ministerstwo Nauki i Szkolnictwa Wyzszego

Narodowe Centrum Nauki

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference77 articles.

1. Advanced evaluation techniques in interferometry;Schwider,1990

2. Optical shop testing;Malacara,2007

3. Interferogram analysis: digital fringe pattern measurement;Robinson,1993

4. Interferogram analysis for optical testing;Malacara,1998

5. Digital holographic microscopy: principles, techniques, and applications;Kim,2011

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1. End-to-End Direct Phase Retrieval From a Single-Frame Interferogram Based on Deep Learning;IEEE Transactions on Instrumentation and Measurement;2024

2. Dual-stage hybrid network for single-shot fringe projection profilometry based on a phase-height model;Optics Express;2023-12-22

3. Physics-driven universal twin-image removal network for digital in-line holographic microscopy;Optics Express;2023-12-21

4. DeepVID: deep-learning accelerated variational image decomposition model tailored to fringe pattern filtration;Journal of Optics;2023-03-09

5. Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects;Opto-Electronic Advances;2023

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