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  4. Defect Detection from 3D Ultrasonic Measurements Using Matrix-free Sparse Recovery Algorithms
 
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2018
Conference Paper
Title

Defect Detection from 3D Ultrasonic Measurements Using Matrix-free Sparse Recovery Algorithms

Abstract
In this paper, we propose an efficient matrix-free algorithm to reconstruct locations and size of flaws in a specimen from volumetric ultrasound data by means of a native 3D Sparse Signal Recovery scheme using Orthogonal Matching Pursuit (OMP). The efficiency of the proposed approach is achieved in two ways. First, we formulate the dictionary matrix as a block multilevel Toeplitz matrix to minimize redundancy and thus memory consumption. Second, we exploit this specific structure in the dictionary to speed up the correlation step in OMP, which is implemented matrix-free. We compare our method to state-of-the-art, namely 3D Synthetic Aperture Focusing Technique, and show that it delivers a visually comparable performance, while it gains the additional freedom to use further methods such as Compressed Sensing.
Author(s)
Semper, Sebastian
Institute for Information Technology, Technische Universität Ilmenau
Kirchhof, Jan
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Wagner, Christoph
Institute for Information Technology, Technische Universität Ilmenau
Krieg, Fabian  
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Römer, Florian  
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Osman, Ahmad  
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Galdo, Giovanni del  
Fraunhofer-Institut für Integrierte Schaltungen IIS  
Mainwork
26th European Signal Processing Conference, EUSIPCO 2018  
Conference
European Signal Processing Conference (EUSIPCO) 2018  
DOI
10.23919/EUSIPCO.2018.8553074
Language
English
Fraunhofer-Institut für Integrierte Schaltungen IIS  
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Keyword(s)
  • Orthogonal Matching Pursuit (OMP)

  • Compressed Sensing (CS)

  • 3D synthetic aperture focusing technique

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