Nearly preprocessing-free method for skeletonization of gray-scale electronic speckle pattern interferometry fringe patterns via partial differential equations

Tang, Chen; Lü, Wenjing; Cai, Yuanxue; Han, Lin-Hai; Wang, Gao

doi:10.1364/ol.33.000183

Cited by 24 publications

(6 citation statements)

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“…The choice of morphological operations depends upon the quality of the images as the images may be degraded during thresholding if proper threshold values are not used or the recorded images may be highly affected by environmental disturbances [19]. We used black chart paper as a screen, as it absorbs undesired reflections of light and reduces the undesirable vignetting effect of cameras [20].…”

Section: Fringe Center Methodsmentioning

confidence: 99%

A comparison of image processing techniques for optical interference fringe analysis

et al. 2015

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We present a comparison of different techniques for the analysis of the shift and tilt in optical interference fringes. Fringe center, Radon transform, and Gaussian approximation methods are used for fringe analysis. We have measured the tilt and shift between two relevant fringe patterns. The error in tilt measurement was about 2%, and the displacement of the order of few nanometers was measured by the fringe shift analysis. The comparison between the techniques is analyzed with respect to percentage error.

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Section: Fringe Center Methodsmentioning

confidence: 99%

A comparison of image processing techniques for optical interference fringe analysis

et al. 2015

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“…A typically effective approach is to utilize fringe-orientation maps, which are usually calculated from gradients or derivatives of the fringe patterns [30,31] or obtained alternatively by use of spin filters [32,33]. Another solution is to determine the possibility for each pixel to be on a skeleton, according to the gradient vector field resulting from a partial differential equation model [34].…”

Section: Introductionmentioning

confidence: 99%

Principal-vector-directed fringe-tracking technique

Zhang

Guo

2014

Appl. Opt.

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Fringe tracking is one of the most straightforward techniques for analyzing a single fringe pattern. This work presents a principal-vector-directed fringe-tracking technique. It uses Gaussian derivatives for estimating fringe gradients and uses hysteresis thresholding for segmenting singular points, thus improving the principal component analysis method. Using it allows us to estimate the principal vectors of fringes from a pattern with high noise. The fringe-tracking procedure is directed by these principal vectors, so that erroneous results induced by noise and other error-inducing factors are avoided. At the same time, the singular point regions of the fringe pattern are identified automatically. Using them allows us to determine paths through which the "seed" point for each fringe skeleton is easy to find, thus alleviating the computational burden in processing the fringe pattern. The results of a numerical simulation and experiment demonstrate this method to be valid.

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“…Then the skeletons of a fringe pattern are extracted by analyzing the topological structure of GVFs. 10 As far as we know, there have been three kinds of governing PDEs especially proposed for calculating GVFs of ESPI fringe patterns. The coupled nonlinear governing PDEs were first proposed for ESPI fringe patterns with usual uniform density.…”

mentioning

confidence: 99%

“…The coupled nonlinear governing PDEs were first proposed for ESPI fringe patterns with usual uniform density. 10 The oriented couple PDEs for high-density fringe were proposed. 11 The anisotropic PDEs were also proposed for uniform density.…”

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confidence: 99%

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Fully automated extraction of the fringe skeletons in dynamic electronic speckle pattern interferometry using a U-Net convolutional neural network

Tang

Zheng

et al. 2019

Opt. Eng.

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With the development of artificial intelligence technology, intelligent fringe processing is a goal of relevant researchers in optical interferometry. We propose an intelligent method to achieve fully automated extraction of the fringe skeletons in electronic speckle pattern interferometry (ESPI) based on U-Net convolutional neural network. In the proposed method, the network is first trained by the samples that consist of the noisy ESPI fringe patterns and the corresponding skeleton images. After training, the other multiframe ESPI fringe patterns are fed to the trained network simultaneously; the corresponding skeleton images can be obtained in batches. Using our method, it is not necessary to process fringe patterns frame by frame. Our method is especially suitable for multiframe fringe patterns processing. We apply the intelligent method to one computer-simulated and one real-dynamic ESPI measurement, respectively. For the simulated measurement, it takes just 40 s to obtain the skeleton images of 20 noisy ESPI fringe patterns using our method. Even for low-quality experimental obtained ESPI fringe patterns, our method can also give desired results.

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Nearly preprocessing-free method for skeletonization of gray-scale electronic speckle pattern interferometry fringe patterns via partial differential equations

Cited by 24 publications

References 0 publications

A comparison of image processing techniques for optical interference fringe analysis

A comparison of image processing techniques for optical interference fringe analysis

Principal-vector-directed fringe-tracking technique

Fully automated extraction of the fringe skeletons in dynamic electronic speckle pattern interferometry using a U-Net convolutional neural network

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