A sparse reconstruction algorithm for ultrasonic images in nondestructive testing.

Autor: Guarneri GA; Graduate School on Electrical Engineering and Applied Computer Science, Federal University of Technology-Paraná (UTFPR), Curitiba-PR 80230-901, Brazil. giovanni@utfpr.edu.br., Pipa DR; Graduate School on Electrical Engineering and Applied Computer Science, Federal University of Technology-Paraná (UTFPR), Curitiba-PR 80230-901, Brazil. danielpipa@utfpr.edu.br., Neves Junior F; Graduate School on Electrical Engineering and Applied Computer Science, Federal University of Technology-Paraná (UTFPR), Curitiba-PR 80230-901, Brazil. neves@utfpr.edu.br., de Arruda LV; Graduate School on Electrical Engineering and Applied Computer Science, Federal University of Technology-Paraná (UTFPR), Curitiba-PR 80230-901, Brazil. lvrarruda@utfpr.edu.br., Zibetti MV; Graduate School on Electrical Engineering and Applied Computer Science, Federal University of Technology-Paraná (UTFPR), Curitiba-PR 80230-901, Brazil. marcelozibetti@utfpr.edu.br.
Jazyk: angličtina
Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2015 Apr 21; Vol. 15 (4), pp. 9324-43. Date of Electronic Publication: 2015 Apr 21.
DOI: 10.3390/s150409324
Abstrakt: Ultrasound imaging systems (UIS) are essential tools in nondestructive testing (NDT). In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, ω-k SAFT and regularized least squares (RLS). The method demonstrates significant resolution improvement when compared with B-scan-about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR).
Databáze: MEDLINE
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