Full Text:
<2964>
CLC number: TB55
On-line Access:
Received: 2006-03-01
Revision Accepted: 2006-07-17
Crosschecked: 0000-00-00
Cited: 3
Clicked: 4996
Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation
| |||||||||||||
HERRERA Roberto Henry, OROZCO Rubén, RODRIGUEZ Manuel. Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation[J]. Journal of Zhejiang University Science A, 2006, 7(10): 1748-1756.
@article{title="Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation",
author="HERRERA Roberto Henry, OROZCO Rubén, RODRIGUEZ Manuel",
journal="Journal of Zhejiang University Science A",
volume="7",
number="10",
pages="1748-1756",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A1748"
}
%0 Journal Article
%T Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation
%A HERRERA Roberto Henry
%A OROZCO Rubé
%A n
%A RODRIGUEZ Manuel
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 10
%P 1748-1756
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A1748
TY - JOUR
T1 - Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation
A1 - HERRERA Roberto Henry
A1 - OROZCO Rubé
A1 - n
A1 - RODRIGUEZ Manuel
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 10
SP - 1748
EP - 1756
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A1748
Abstract: In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener filter is used to obtain the ultrasonic reflectivity function through wavelet-based models. A new approach to the parameter estimation of the inverse filtering step is proposed in the nondestructive evaluation field, which is based on the theory of Fourier-Wavelet regularized deconvolution (ForWaRD). This new approach can be viewed as a solution to the open problem of adaptation of the ForWaRD framework to perform the convolution kernel estimation and deconvolution interdependently. The results indicate stable solutions of the estimated pulse and an improvement in the radio-frequency (RF) signal taking into account its signal-to-noise ratio (SNR) and axial resolution. Simulations and experiments showed that the proposed approach can provide robust and optimal estimates of the reflectivity function.
[1] Abeyratne, U.R., Petropulu, A.P., Reid, J.M., 1995. Higher order spectra based deconvolution of ultrasound images. IEEE Trans. Ultrason. Ferroelect. Freq. Contr., 42(6):1064-1075.
[2] Adam, D., Michailovich, O., 2002. Blind deconvolution of ultrasound sequences using nonparametric local polynomial estimates of the pulse. IEEE Transactions on Biomedical Engineering, 49(2):118-131.
[3] Donoho, D.L., 1995. De-noising by soft-thresholding. IEEE Trans. Inform. Theory, 41(3):613-627.
[4] Honarvar, F., Sheikhzadeh, H., Moles, M., Sinclair, A.N., 2004. Improving the time-resolution and signal-to-noise ratio of ultrasonic NDE signals. Ultrasonics, 41(9):755-763.
[5] Kaaresen, K.F., Bolviken, E., 1999. Blind deconvolution of ultrasonic traces accounting for pulse variance. IEEE Trans. Ultrason. Ferroelect. Freq. Contr., 46(3):564-573.
[6] Mesa, H., 2005. Adapted wavelets for pattern detection. Lecture Notes in Computer Science, 3773:933-944.
[7] Michailovich, O., Adam, D., 2003. Robust estimation of ultrasound pulses using outlier-resistant de-noising. IEEE Trans. on Medical Imaging, 22(3):368-381.
[8] Neelamani, R., Choi, H., Baraniuk, R., 2004. ForWaRD: fourier-wavelet regularized deconvolution for ill-conditioned systems. IEEE Trans. Ultrason. Ferroelect. Freq. Contr., 52(2):418-432.
[9] Oppenheim, A.V., Schafer, R.W., 1989. Discrete Time Signal Processing. London Prentice-Hall, p.768-825.
[10] Pan, R., Nikias, C., 1988. The complex cepstrum of higher order cumulants and nonminimum phase system identification. IEEE Trans. ASSP, 36(2):186-205.
[11] Swami, A., Mendel, J.M., Nikias, C.L., 1998. Higher-order Spectral Analysis Toolbox for Use with Matlab. Http://www.mathworks.com
[12] Taxt, T., 1997. Comparison of cepstrum-based methods for radial blind deconvolution of ultrasound images. IEEE Trans. Ultrason. Ferroelect. Freq. Contr., 44(3):666-674.
[13] Wan, S., Raju, B.I., Srinivasan, M.A., 2003. Robust deconvolution of high-frequency ultrasound images using higher-order spectral analysis and wavelets. IEEE Trans. Ultrason. Ferroelect. Freq. Contr., 50(10):1286-1295.
Open peer comments: Debate/Discuss/Question/Opinion
<1>