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Sensors 2016, 16(10), 1773; doi:10.3390/s16101773

Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging

1
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2
Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, Wuxi 214122, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Dipen N. Sinha and Cristian Pantea
Received: 28 July 2016 / Revised: 30 September 2016 / Accepted: 12 October 2016 / Published: 24 October 2016
(This article belongs to the Special Issue Ultrasonic Sensors)
View Full-Text   |   Download PDF [2854 KB, uploaded 24 October 2016]   |  

Abstract

Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l1-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging. View Full-Text
Keywords: sparse reconstruction; micro defect; acoustic micro-imaging; point spread function sparse reconstruction; micro defect; acoustic micro-imaging; point spread function
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, Y.; Shi, T.; Su, L.; Wang, X.; Hong, Y.; Chen, K.; Liao, G. Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging. Sensors 2016, 16, 1773.

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