Speeding up 3D SAFT for ultrasonic NDT by sparse deconvolution

Kirchhof J, Krieg F, Romer F, Ihlow A, Osman A, Del Galdo G (2016)


Publication Type: Conference contribution

Publication year: 2016

Journal

Publisher: IEEE Computer Society

Book Volume: 2016-November

Conference Proceedings Title: IEEE International Ultrasonics Symposium, IUS

Event location: Tours, FRA

ISBN: 9781467398978

DOI: 10.1109/ULTSYM.2016.7728434

Abstract

In this paper we propose to pre-process ultrasonic measurements (A-scans) in Non-Destructive Testing (NDT) by sparse deconvolution before post-processing the data with the Synthetic Aperture Focusing Technique (SAFT). Compared to state-of-the-art SAFT post-processing of raw A-scan measurements, pre-processing by sparse deconvolution can improve NDT in the following ways: First, the temporal resolution of signal reflections is increased. Second, because the A-scans appear as a sparse signal of spikes, it is possible to formulate the time-domain SAFT algorithm in a new fashion that is both faster compared to conventional SAFT and the deconvolved input data can be focussed better leading to a higher resolution. Since sparse deconvolution could be implemented directly into the ultrasonic probe hardware/software measurement setup, this approach can significantly speed up measurements in time-critical environments. We test the proposed scheme on CIVA simulation data as well as measurements and show B- and C-images of raw SAFT vs. Orthogonal Matching Pursuit (OMP) + SAFT and Basis Pursuit Denoising (BPDN) + SAFT.

Involved external institutions

How to cite

APA:

Kirchhof, J., Krieg, F., Romer, F., Ihlow, A., Osman, A., & Del Galdo, G. (2016). Speeding up 3D SAFT for ultrasonic NDT by sparse deconvolution. In IEEE International Ultrasonics Symposium, IUS. Tours, FRA: IEEE Computer Society.

MLA:

Kirchhof, Jan, et al. "Speeding up 3D SAFT for ultrasonic NDT by sparse deconvolution." Proceedings of the 2016 IEEE International Ultrasonics Symposium, IUS 2016, Tours, FRA IEEE Computer Society, 2016.

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