Automatized fringe pattern preprocessing using unsupervised variational image decomposition

doi:10.1364/OE.27.022542

2019-07-24

Maria Cywińska *

Maciej Trusiak

Krzysztof Patorski

DOI：10.1364/OE.27.022542

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摘要

En 中文

Successful single-frame fringe pattern preprocessing comprising high-frequency noise minimization and low-frequency background removal represents often the crucial step of the fringe pattern based full-field optical metrology (i.e., interferometry, moire, structured light). It directly determines the measurement accuracy. Data-driven decomposition by means of the 2D empirical mode decomposition (EMD) serves the filtering purpose in adaptive and detail-preserving manner. The mode-mixing phenomenon resulting in troublesome automatic grouping of modes into three main fringe pattern components (background, information part and noise) is significantly limiting this process, however. In this paper we are introducing the unsupervised variational image decomposition (uVID) model especially tailored to overcome this preprocessing problem and assure successful sparse three-component fringe pattern decomposition. Comprehensive analysis and detailed studies of accomplished significant advancements ensuring automation, versatility and robustness of the proposed approach are provided. Main advancements include: (1) tailoring the VID calculation scheme to fringe pattern preprocessing purpose by focusing onto accurate fringe extraction with tolerance parameter and custom-made decomposition parameter values: (2) fringe pattern tailored BM3D denoising algorithm with fixed parameter values. Numerical and experimental investigations corroborate that the demonstrated uVID method compares favorably with the reference 2D EMD algorithm and classical VID model. Remarkable range of acceptable local variations of the fringe pattern orientation, period, noise, contrast and background terms is to be highlighted. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Keyword:

EMPIRICAL MODE DECOMPOSITION

TOTAL VARIATION MINIMIZATION

NOISE REMOVAL

SPECKLE NOISE

ALGORITHM

DEMODULATION

ENHANCEMENT

REDUCTION

RETRIEVAL

WAVELET