SoS filter and its application to industrial ultrasound imaging

被引：0

作者：

Dai, Guang-Zhi ^{[1
]}

Han, Guo-Qiang ^{[1
]}

Lin, Wei-Yi ^{[2
]}

Ouyang, Xian-Yue ^{[3
]}

机构：

[1] School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, Guangdong

[2] School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, Guangdong

[3] Key Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha 410082, Hunan

来源：

Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2014年 / 42卷 / 01期

关键词：

Compressed sensing; Finite rate of innovation; Industrial ultrasound imaging; Sampling data; Sampling frequency; Signal processing; Sum of Sinc Filter;

D O I：

10.3969/j.issn.1000-565X.2014.01.019

中图分类号：

学科分类号：

摘要：

Although the FRI (Finite Rate of Innovation) model may be feasible in reducing the sampling data and sampling frequency of industrial ultrasound imaging systems, the existing FRI model cannot be well applied due to the high frequency of reflected signals. In order to solve this problem, a new FRI acquisition method named Sum of Sinc (SoS) filter, which is very suitable for the processing of industrial ultrasound signals, is proposed on the basis of the FRI filter group. Afterwards, the necessary conditions for this method are deduced, and the structure of the SoS filter is presented. From the results of raw one-dimension ultrasound imaging data, the proposed method is found to be effective and feasible.

引用

页码：111 / 116

页数：5

共 16 条

[1]

Donoho D.L., Compressed sensing, IEEE Transactions on Information Theory, 52, 4, pp. 1289-1306, (2006)

[2]

Candes E.J., Romberg J., Tao T., Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, 52, 2, pp. 489-509, (2006)

[3]

Candes E.J., Compressive sampling, Proceedings of the International Congress of Mathematics, pp. 1433-1452, (2006)

[4]

Candes E.J., Wakin M.B., An introduction to compressive sampling, IEEE Signal Processing Magazine, 25, 2, pp. 21-30, (2008)

[5]

Tur R., Eldar Y.C., Friedman Z., Innovation rate sampling of pulse streams with application to ultrasound imaging, IEEE Transactions on Signal Processing, 59, 4, pp. 1827-1842, (2011)

[6]

Wagner N., Eldar Y.C., Feuer A., Et al., Compressed beamforming in ultrasound imaging, IEEE Transactions on Signal Processing, 60, 9, pp. 4643-4657, (2012)

[7]

Chernyakova T., Eldar Y.C., Fourier domain beamforming: the path to compressed ultrasound imaging, (2013)

[8]

Wagner N., Eldar Y.C., Feuer A., Et al., Compressed beamforming with applications to ultrasound imaging, Proceedings of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3641-3644, (2012)

[9]

Friboulet D., Liebgott H., Prost R., Compressive sensing for raw RF signals reconstruction in ultrasound, Proceedings of 2010 IEEE Ultrasonics Symposium, pp. 367-370, (2010)

[10]

Quinsac C., Basarab A., Kouame D., Frequency domain compressive sampling for ultrasound imaging, Advances in Acoustics and Vibration, 2012, (2012)

← 1 2 →