A 3D shape retrieval method for orthogonal fringe projection based on a combination of variational image decomposition and variational mode decomposition

Li, Biyuan; Tang, Chen; Zhu, Xinjun; Xia, Chen; Su, Yonggang; Cai, Yuanxue

doi:10.1016/j.optlaseng.2016.06.020

Cited by 16 publications

(8 citation statements)

References 20 publications

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“…All modes are determined simultaneously. This is why VMD is currently drawing increased attention [27,28,30] . This algorithm deserves attention in future studies.…”

Section: Discussionmentioning

confidence: 99%

Bidimensional unconstrained optimization approach to EMD: An algorithm revealing skin perfusion alterations in pseudoxanthoma elasticum patients

Humeau-Heurtier

Colominas

Schlotthauer

et al. 2017

Computer Methods and Programs in Biomedicine

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“…All modes are determined simultaneously. This is why VMD is currently drawing increased attention [27,28,30] . This algorithm deserves attention in future studies.…”

Section: Discussionmentioning

confidence: 99%

Bidimensional unconstrained optimization approach to EMD: An algorithm revealing skin perfusion alterations in pseudoxanthoma elasticum patients

Humeau-Heurtier

Colominas

Schlotthauer

et al. 2017

Computer Methods and Programs in Biomedicine

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“…In the 2D HHT, the 2D empirical mode decomposition (EMD) based techniques (Huang's part of the transform) were successfully employed for fringe pattern pre-filtering [12][13][14][15][16][17]. VID can serve this purpose as well [22][23][24][25]. Residual preprocessing imperfections affect the demodulated phase distribution calculated using the 2D Hilbert spiral transform (HST) [17][18][19][20], however.…”

Section: The Hilbert-huang Transform-based Single-frame Phase Demodul...mentioning

confidence: 99%

“…Variational image decomposition is a relatively new method for background and noise removal from fringe patterns [22][23][24][25]. In this method the image is split into three components: the part containing background (structure), the one associated with fringes (texture), and noise.…”

Section: The Proposed Modified Variational Image Decomposition Techniquementioning

confidence: 99%

“…In this contribution, without a loss of generality, we focus on phase error reduction in the single-frame phase demodulation techniques exemplified by the HHT [15][16][17]. The novel approach presented deploys variational image decomposition (VID) [22][23][24][25] previously used for fringe pre-filtering. The similarity and difference between fringe and phase filtration will be discussed in section 2.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing single-shot fringe pattern phase demodulation using advanced variational image decomposition

Cywińska

Trusiak

Zuo

et al. 2019

J. Opt.

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In many full-field optical metrology techniques, i.e. interferometry, moiré, and structured light, the information about the measurand, e.g. displacement, strain, or 3D shape, is stored in the phase distribution of a recorded two-dimensional intensity pattern—the fringe pattern. Its analysis (phase demodulation) therefore plays a crucial role in the measurement procedure, significantly affecting the total accuracy of the optical system. Phase demodulation methods based on just a single fringe pattern are especially interesting, due to their robustness to environmental disturbances and ability to examine dynamic events. However, the calculated phase map is easily spoiled by errors, which appear mainly because of fringe pattern imperfections, i.e. random noise, parasitic interferences, a nonsinusoidal fringe pattern profile or a non-uniform image background. In this contribution, an advanced variational image decomposition scheme is proposed to reduce these phase demodulation errors. The reported purely phase domain method can be easily adopted to aid virtually any fringe analysis method, including single-frame and multi-frame phase-shifting, possibly enhancing retrieved phase distribution without the need for hardware manipulation. We employed it to improve single-frame Hilbert–Huang transform-based fringe analysis. The remarkable efficiency and versatility of the developed algorithm are verified by processing synthetic and experimental fringe patterns and phase maps. The demonstrated approach compares favorably with the very capable 2D empirical mode decomposition reference method.

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“…However, phase retrieval from single frame fringe pattern is a challenging problem in fringe projection 3D measurement especially for objects with edges or abrupt changes in depth, which attracts wide attention. Numerous methods have been proposed, such as the well-known Fourier Transform method (FT), Windowed Fourier Transform method (WFT), the Wavelet Transform method (WT), Shearlet Transform method (ST), and the more effective methods such as Empirical Mode Decomposition (EMD) method and more recently proposed variational image decomposition(VID) and variational mode decomposition (VMD) methods [8][9][10][11][12][13][14][15]. Although #372727 https://doi.org/10.1364/OE.27.028929 extensive research efforts have been made for phase retrieval, it is hard to implement an accurate and fast retrieval phase method for the tradeoff between the accuracy and computational efficiency in traditional phase retrieval methods.…”

Section: Introductionmentioning

confidence: 99%

Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement

et al. 2019

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We propose a label enhanced and patch based deep learning phase retrieval approach which can achieve fast and accurate phase retrieval using only several fringe patterns as training dataset. To the best of our knowledge, it is the first time that the advantages of the label enhancement and patch strategy for deep learning based phase retrieval are demonstrated in fringe projection. In the proposed method, the enhanced labeled data in training dataset is designed to learn the mapping between the input fringe pattern and the output enhanced fringe part of the deep neural network (DNN). Moreover, the training data is cropped into small overlapped patches to expand the training samples for the DNN. The performance of the proposed approach is verified by experimental projection fringe patterns with applications in dynamic fringe projection 3D measurement.

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A 3D shape retrieval method for orthogonal fringe projection based on a combination of variational image decomposition and variational mode decomposition

Cited by 16 publications

References 20 publications

Bidimensional unconstrained optimization approach to EMD: An algorithm revealing skin perfusion alterations in pseudoxanthoma elasticum patients

Bidimensional unconstrained optimization approach to EMD: An algorithm revealing skin perfusion alterations in pseudoxanthoma elasticum patients

Enhancing single-shot fringe pattern phase demodulation using advanced variational image decomposition

Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement

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