Research Progress in Fringe Image Processing Algorithms

Xiyong Yuan

doi:10.47297/taposatWSP2633-456914.20260707

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Research Progress in Fringe Image Processing Algorithms

Volume 7, Issue 7

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Research Progress in Fringe Image Processing Algorithms
Research Progress in Fringe Image Processing Algorithms
Mechanical Engineering School, University of Shanghai for Science and Technology, Shanghai, 200093, China
Vol. 7, Issue 7, Pages: 76-80(2026)
Online First：21 August 2026，

Published：30 July 2026
DOI：10.47297/taposatWSP2633-456914.20260707
Accepted：

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Yuan Xiyong.Research Progress in Fringe Image Processing Algorithms[J].Theory and Practice of Science and Technology,2026,07(07):76-80.
DOI：

Yuan Xiyong.Research Progress in Fringe Image Processing Algorithms[J].Theory and Practice of Science and Technology,2026,07(07):76-80. DOI： 10.47297/taposatWSP2633-456914.20260707.

摘要

Abstract

Fringe images are key information carriers in optical interferometry

profilometry

and deformation measurement. Their phase distributions encode displacement

shape

and strain

whereas practical recordings also contain nonuniform background

modulation variations

noise

phase-shift errors

and acquisition disturbances. Reliable recovery therefore requires both phase demodulation and suppression of non-phase components. This review summarizes four major families: frequency-domain and time-frequency analysis

phase-shifting and parameter estimation

statistical subspace decomposition

and deep learning. Their underlying assumptions

representative developments

strengths

and limitations are compared. Frequency-domain methods remain attractive for single-frame processing but are sensitive to spectral overlap and local nonstationarity. Phase-shifting and optimization methods offer clear physical interpretability but depend on acquisition consistency and model validity. Statistical subspace methods reduce reliance on accurately controlled phase steps

although their stability deteriorates under strong background and modulation variations. Deep learning provides powerful nonlinear representation capabilities

yet its generalization and interpretability remain unresolved. Current progress increasingly favors hybrid frameworks that combine physical models

statistical estimation

adaptive optimization

and data-driven priors.

关键词

Keywords

references

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