Next Article in Journal
A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance Problem
Next Article in Special Issue
Detection of Small Lesions on Grape Leaves Based on Improved YOLOv7
Previous Article in Journal
Ka-Band Miniaturized 90 nm Complementary Metal Oxide Semiconductor Wideband Rat-Race Coupler Using Left-Handed and Right-Handed Transmission Lines
Previous Article in Special Issue
A Residual Network with Efficient Transformer for Lightweight Image Super-Resolution
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Deep Learning in the Phase Extraction of Electronic Speckle Pattern Interferometry

by
Wenbo Jiang
1,2,*,
Tong Ren
1,2 and
Qianhua Fu
1,2
1
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China
2
Sichuan Provincial Key Laboratory of Signal and Information Processing, Xihua University, Chengdu 610039, China
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(2), 418; https://doi.org/10.3390/electronics13020418
Submission received: 28 December 2023 / Revised: 15 January 2024 / Accepted: 17 January 2024 / Published: 19 January 2024
(This article belongs to the Special Issue Deep Learning in Image Processing and Pattern Recognition)

Abstract

Electronic speckle pattern interferometry (ESPI) is widely used in fields such as materials science, biomedical research, surface morphology analysis, and optical component inspection because of its high measurement accuracy, broad frequency range, and ease of measurement. Phase extraction is a critical stage in ESPI. However, conventional phase extraction methods exhibit problems such as low accuracy, slow processing speed, and poor generalization. With the continuous development of deep learning in image processing, the application of deep learning in phase extraction from electronic speckle interferometry images has become a critical topic of research. This paper reviews the principles and characteristics of ESPI and comprehensively analyzes the phase extraction processes for fringe patterns and wrapped phase maps. The application, advantages, and limitations of deep learning techniques in filtering, fringe skeleton line extraction, and phase unwrapping algorithms are discussed based on the representation of measurement results. Finally, this paper provides a perspective on future trends, such as the construction of physical models for electronic speckle interferometry, improvement and optimization of deep learning models, and quantitative evaluation of phase extraction quality, in this field.
Keywords: electronic speckle pattern interferometry (ESPI); phase extraction; deep learning; fringe pattern; wrapped phase maps electronic speckle pattern interferometry (ESPI); phase extraction; deep learning; fringe pattern; wrapped phase maps

Share and Cite

MDPI and ACS Style

Jiang, W.; Ren, T.; Fu, Q. Deep Learning in the Phase Extraction of Electronic Speckle Pattern Interferometry. Electronics 2024, 13, 418. https://doi.org/10.3390/electronics13020418

AMA Style

Jiang W, Ren T, Fu Q. Deep Learning in the Phase Extraction of Electronic Speckle Pattern Interferometry. Electronics. 2024; 13(2):418. https://doi.org/10.3390/electronics13020418

Chicago/Turabian Style

Jiang, Wenbo, Tong Ren, and Qianhua Fu. 2024. "Deep Learning in the Phase Extraction of Electronic Speckle Pattern Interferometry" Electronics 13, no. 2: 418. https://doi.org/10.3390/electronics13020418

APA Style

Jiang, W., Ren, T., & Fu, Q. (2024). Deep Learning in the Phase Extraction of Electronic Speckle Pattern Interferometry. Electronics, 13(2), 418. https://doi.org/10.3390/electronics13020418

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop