Hybrid Ultrasonic TOFD Imaging of Weld Flaws Using Wavelet Transforms and Image Registration
Hybrid Ultrasonic TOFD Imaging of Weld Flaws Using Wavelet Transforms and Image Registration
复制标题
使用小波变换和图像配准对焊接缺陷进行混合超声 TOFD 成像
DOI：
10.1007/s10921-018-0476-2
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发表时间：
2017-06
影响因子：
2.8
通讯作者：
Jin-long Li
中科院分区：
文献类型：
--
作者：
Jia-xu Duan;Lin Luo;Xiao-rong Gao;Jian-ping Peng;Jin-long Li
超声衍射时差法（TOFD）是一种有效的焊缝裂纹检测技术。由于衍射波的强度相对于侧向波和海底回波的强度较弱，导致TOFD成像的信噪比较低。本文提出了一种新的二分方法，即小波收缩和图像配准两个步骤来降低TOFD图像的噪声，同时提高TOFD图像的分辨率。为了验证本文方法的可靠性，建立了实验系统，并采集了大量噪声特征随机分布的TOFD数据。在算法的第一步，我们分别采用了一维小波变换和二维小波变换。与经典算法相比，该步得到的结果的信噪比有了明显的提高。接下来，应用图像配准。将配准后的图像进行叠加，形成新的配准图像，最终的结果表明，该方法不仅有效地提高了图像的信噪比，而且有效地提高了图像的清晰度。
Ultrasonic time of flight diffraction (TOFD) is an effective weld crack inspection technique. Due to the intensity of diffraction wave is rather weak compared with the lateral wave and the bottom echo wave, thus the signal-to-ratio (SNR) of TOFD image is low. A new dichotomous method is comprised of two steps that contains wavelet shrinkage and image registration is proposed in this paper to reduce the noise and improve the resolution of TOFD images as well. In order to evaluate the reliability of our proposed method in this paper, we have established the experiment system, and sampled a number of TOFD data with random distribution of noise characteristics. We adopted one-dimension wavelet transform and two-dimension wavelet transform in the very beginning of the first step of the proposed algorithm respectively. The SNR of the result obtained in this step is improved significantly compared with the classic algorithms. Next, the image registration is applied. After the registered images have been added to form a new one, then it comes to the final result that shows not only the SNR but also the definition of the image is enhanced effectively.
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影响因子：
5.6
作者：
Mohammad R. Hoseini;M. Zuo;Xiaodong Wang
通讯作者：
Mohammad R. Hoseini;M. Zuo;Xiaodong Wang
影响因子：
4.2
作者：
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通讯作者：
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DOI：
10.5120/12385-8737
发表时间：
2013-06
期刊：
International Journal of Computer Applications
影响因子：
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作者：
A. Praveen;K. Vijayarekha;Saju T. Abraham;B. Venkatraman
通讯作者：
A. Praveen;K. Vijayarekha;Saju T. Abraham;B. Venkatraman
影响因子：
6.4
作者：
Pillonetto, Gianluigi;Bell, Bradley M.
通讯作者：
Bell, Bradley M.
影响因子：
4.2
作者：
L. Ericsson;T. Stepinski
通讯作者：
L. Ericsson;T. Stepinski