Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Liang, Jian; Zhang, Junchao; Shao, Jianbo; Song, Bofan; Liang, Rongguang

doi:10.3390/s20133691

Cited by 34 publications

(17 citation statements)

References 20 publications

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“…For image edge extraction, many methods have been proposed. Although the FCN (fully convolutional network) can identify the spatial relationship between pixels and realize the segmentation at the semantic level, the process of restoring the image is relatively simple and the obtained fringe boundary is not accurate enough (Jonatnan et al, 2014;Liang et al, 2020). As for InSAR interferometric images, there are not a large amount of training data available for public use, so we can only collect and generate them by ourselves, and the number of samples generated is generally small.…”

Section: Extracting the Edge Of Interferometric Fringesmentioning

confidence: 99%

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

Wang

Lü

et al. 2021

Front. Earth Sci.

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Large-scale and high-intensity mining underground coal has resulted in serious land subsidence. It has caused a lot of ecological environment problems and has a serious impact on the sustainable development of economy. Land subsidence cannot be accurately monitored by InSAR (interferometric synthetic aperture radar) due to the low coherence in the mining area, excessive deformation gradient, and the atmospheric effect. In order to solve this problem, a novel phase unwrapping method based on U-Net convolutional neural network was constructed. Firstly, the U-Net convolutional neural network is used to extract edge to automatically obtain the boundary information of the interferometric fringes in the region of subsidence basin. Secondly, an edge-linking algorithm is constructed based on edge growth and predictive search. The interrupted interferometric fringes are connected automatically. The whole and continuous edges of interferometric fringes are obtained. Finally, the correct phase unwrapping results are obtained according to the principle of phase unwrapping and the wrap-count (integer jump of 2π) at each pixel by edge detection. The Huaibei Coalfield in China was taken as the study area. The real interferograms from D-InSAR (differential interferometric synthetic aperture radar) processing used Sentinel-1A data which were used to verify the performance of the new method. Subsidence basins with clear interferometric fringes, interrupted interferometric fringes, and confused interferometric fringes are selected for experiments. The results were compared with the other methods, such as MCF (minimum cost flow) method. The tests showed that the new method based on U-Net convolutional neural network can resolve the problem that is difficult to obtain the correct unwrapping phase due to interrupted or partially confused interferometric fringes caused by low coherence or other reasons in the coal mining area. Hence, the new method can help to accurately monitor the subsidence in mining areas under different conditions using InSAR technology.

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Section: Extracting the Edge Of Interferometric Fringesmentioning

confidence: 99%

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

Wang

Lü

et al. 2021

Front. Earth Sci.

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Section: Introductionmentioning

confidence: 99%

“…In recent years, the deep learning-based phase unwrapping methods have attracted significant interest [16][17][18][19][20][21][22][23][24]. Most of these methods [16][17][18] convert the unwrapping problem into a classification problem of the wrap count, and their effectiveness is verified using optical images. In the field of InSAR, the unwrapping problem becomes more difficult because of two characteristics: the complex wrapped phase caused by topography features and the low coherence coefficient.…”

Section: Introductionmentioning

confidence: 99%

A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network

Zhou

et al. 2021

Remote Sensing

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Phase unwrapping is a critical step in synthetic aperture radar interferometry (InSAR) data processing chains. In almost all phase unwrapping methods, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step. The phase continuity assumption is not always satisfied due to the presence of noise and abrupt terrain changes; therefore, it is difficult to get the correct phase gradient. In this paper, we propose a robust least squares phase unwrapping method that works via a phase gradient estimation network based on the encoder–decoder architecture (PGENet) for InSAR. In this method, from a large number of wrapped phase images with topography features and different levels of noise, the deep convolutional neural network can learn global phase features and the phase gradient between adjacent pixels, so a more accurate and robust phase gradient can be predicted than that obtained by PGE-PCA. To get the phase unwrapping result, we use the traditional least squares solver to minimize the difference between the gradient obtained by PGENet and the gradient of the unwrapped phase. Experiments on simulated and real InSAR data demonstrated that the proposed method outperforms the other five well-established phase unwrapping methods and is robust to noise.

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“…Based on the successful applications of deep learning in the computer vision and optics fields, integrating the structured-light technique and the deep learning scheme for accurate 3D shape reconstruction should be achievable [25][26][27]. As a matter of fact, a few strategies have been proposed to transform a captured structured-light image into its corresponding 3D shape using deep learning.…”

Section: Introductionmentioning

confidence: 99%

Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network

Nguyen

Wang

2021

Photonics

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Accurate three-dimensional (3D) shape reconstruction of objects from a single image is a challenging task, yet it is highly demanded by numerous applications. This paper presents a novel 3D shape reconstruction technique integrating a high-accuracy structured-light method with a deep neural network learning scheme. The proposed approach employs a convolutional neural network (CNN) to transform a color structured-light fringe image into multiple triple-frequency phase-shifted grayscale fringe images, from which the 3D shape can be accurately reconstructed. The robustness of the proposed technique is verified, and it can be a promising 3D imaging tool in future scientific and industrial applications.

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Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Cited by 34 publications

References 20 publications

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network

Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network

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