Reconstruction of laser ultrasonic wavefield images from reduced sparse measurements using compressed sensing aided super-resolution

Park, Byeongjin; Sohn, Hoon

doi:10.1063/1.4974571

Cited by 5 publications

(2 citation statements)

References 15 publications

(20 reference statements)

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“…Among learning-based methods, the one based on SRCNN was the first which was applied in wavefield imaging. 26 In this work, we have compared the results achievable with this method with the deep learning strategies which will be introduced in the following section. The architecture of the SRCNN is shown in Figure 4.…”

Section: Srcnnmentioning

confidence: 99%

“…24 Learning based on super-resolution (SR) was shown to be capable of obtaining HR images without any over-smoothing, no ringing and jagged artifacts such as aliasing, blur, and halo around the edges. 25 The first attempt to combine CS and SRCNN in wavefield imaging was done, to the best of the authors’ knowledge, in a conference paper by Park and Sohn, 26 where some first qualitative results were presented. The approach described in Park’s work was based on a training set constituted by generic heterogeneous images.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for enhancing wavefield image quality in fast non-contact inspections

Esfandabadi

Bilodeau

Masson

et al. 2019

Structural Health Monitoring

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Ultrasonic wavefield imaging with a non-contact technology can provide detailed information about the health status of an inspected structure. However, high spatial resolution, often necessary for accurate damage quantification, typically demands a long scanning time. In this work, we investigate a novel methodology to acquire high-resolution wavefields with a reduced number of measurement points to minimize the acquisition time. Such methodology is based on the combination of compressive sensing and convolutional neural networks to recover high spatial frequency information from low-resolution images. A data set was built from 652 wavefield images acquired with a laser Doppler vibrometer describing guided ultrasonic wave propagation in eight different structures, with and without various simulated defects. Out of those 652 images, 326 cases without defect and 326 cases with defect were used as a training database for the convolutional neural network. In addition, 273 wavefield images were used as a testing database to validate the proposed methodology. For quantitative evaluation, two image quality metrics were calculated and compared to those achieved with different recovery methods or by training the convolutional neural network with non-wavefield images data set. The results demonstrate the capability of the technique for enhancing image resolution and quality, as well as similarity to the wavefield acquired on the full high-resolution grid of scan points, while reducing the number of measurement points down to 10% of the number of scan points for a full grid.

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Section: Srcnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning for enhancing wavefield image quality in fast non-contact inspections

Esfandabadi

Bilodeau

Masson

et al. 2019

Structural Health Monitoring

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Sparse Reconstruction and Damage Imaging Method Based on Uniform Sparse Sampling

Luo

Feng

et al. 2020

Acta Mech. Solida Sin.

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The full wavefield detection method based on guided waves can efficiently detect and locate damages relying on the collection of large amounts of wavefield data. The acquisition process by scanning laser Doppler vibrometer (SLDV) is generally time-consuming, which is limited by Nyquist sampling theorem. To reduce the acquisition time, full wavefield data can be reconstructed from a small number of random sampling point signals combining with compressed sensing. However, the random sampling point signals need to be obtained by adding additional components to the SLDV system or offline processing. Because the random sparse sampling is difficult to achieve via the SLDV system, a new uniform sparse sampling strategy is proposed in this paper. By using the uniform sparse sampling coordinates instead of the random spatial sampling point coordinates, sparse sampling can be applied to SLDV without adding additional components or offline processing. The simulation and experimental results show that the proposed strategy can reduce the measurement locations required for accurate signal recovery to less than 90% of the Nyquist sampling grid, and the damage location error is within the minimum half wavelength. Compared with the conventional jittered sampling strategy, the proposed sampling strategy can directly reduce the sampling time of the SLDV system by more than 90% without adding additional components and achieve the same accuracy of guided wavefield reconstruction and damage location as the jittered sampling strategy. The research results can greatly improve the efficiency of damage detection technology based on wavefield analysis.

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Sensing solutions for assessing and monitoring of nuclear power plants

Park

Yang²,

Lee³

et al. 2022

Sensor Technologies for Civil Infrastructures

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Reconstruction of laser ultrasonic wavefield images from reduced sparse measurements using compressed sensing aided super-resolution

Cited by 5 publications

References 15 publications

Deep learning for enhancing wavefield image quality in fast non-contact inspections

Deep learning for enhancing wavefield image quality in fast non-contact inspections

Sparse Reconstruction and Damage Imaging Method Based on Uniform Sparse Sampling

Sensing solutions for assessing and monitoring of nuclear power plants

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