Literature DB >> 12656358

Imaging in the presence of grain noise using the decomposition of the time reversal operator.

E Kerbrat1, C Prada, D Cassereau, M Fink.   

Abstract

In this paper, we are interested in detecting and imaging defects in samples of cylindrical geometry with large speckle noise due to the microstructure. The time reversal process is an appropriate technique for detecting flaws in such heterogeneous media as titanium billets. Furthermore, time reversal can be iterated to select the defect with the strongest reflectivity and to reduce the contribution of speckle noise. The DORT (the French acronym for Decomposition of the Time Reversal Operator) method derives from the mathematical analysis of the time reversal process. This detection technique allows the determination of a set of signals to be applied to the transducers in order to focus on each defect separately. In this paper, we compare three immersion techniques on a titanium sample, standard transmit/receive focusing, the time reversal mirror (TRM), and the DORT method. We compare the sensitivity of these three techniques, especially the sensitivity to a poor alignment of the array with the front face of the sample. Then we show how images of the sample can be obtained with the TRM and the DORT method using backpropagation algorithm.

Year:  2003        PMID: 12656358     DOI: 10.1121/1.1548156

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  2 in total

1.  The detection of flaws in austenitic welds using the decomposition of the time-reversal operator.

Authors:  Laura J Cunningham; Anthony J Mulholland; Katherine M M Tant; Anthony Gachagan; Gerry Harvey; Colin Bird
Journal:  Proc Math Phys Eng Sci       Date:  2016-04       Impact factor: 2.704

2.  An Ultrasonic Reverse Time Migration Imaging Method Based on Higher-Order Singular Value Decomposition.

Authors:  Yuncheng Zhang; Xiang Gao; Jiawei Zhang; Jingpin Jiao
Journal:  Sensors (Basel)       Date:  2022-03-25       Impact factor: 3.576

  2 in total

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