Zhang Guang-Ming, Harvey David M, Braden Derek R
General Engineering Research Institute, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom.
J Acoust Soc Am. 2008 Nov;124(5):2963-72. doi: 10.1121/1.2982414.
Sparse signal representations from overcomplete dictionaries are the most recent technique in the signal processing community. Applications of this technique extend into many fields. In this paper, this technique is utilized to cope with ultrasonic flaw detection and noise suppression problem. In particular, a noisy ultrasonic signal is decomposed into sparse representations using a sparse Bayesian learning algorithm and an overcomplete dictionary customized from a Gabor dictionary by incorporating some a priori information of the transducer used. Nonlinear postprocessing including thresholding and pruning is then applied to the decomposed coefficients to reduce the noise contribution and extract the flaw information. Because of the high compact essence of sparse representations, flaw echoes are packed into a few significant coefficients, and noise energy is likely scattered all over the dictionary atoms, generating insignificant coefficients. This property greatly increases the efficiency of the pruning and thresholding operations and is extremely useful for detecting flaw echoes embedded in background noise. The performance of the proposed approach is verified experimentally and compared with the wavelet transform signal processor. Experimental results to detect ultrasonic flaw echoes contaminated by white Gaussian additive noise or correlated noise are presented in the paper.
过完备字典的稀疏信号表示是信号处理领域的最新技术。该技术的应用扩展到许多领域。本文利用该技术来解决超声探伤和噪声抑制问题。具体而言，使用稀疏贝叶斯学习算法和通过合并所用换能器的一些先验信息从伽柏字典定制的过完备字典，将有噪声的超声信号分解为稀疏表示。然后对分解后的系数应用包括阈值处理和修剪在内的非线性后处理，以减少噪声贡献并提取缺陷信息。由于稀疏表示具有高度紧凑的本质，缺陷回波被打包到少数几个显著系数中，而噪声能量可能散布在整个字典原子上，产生不显著的系数。这一特性大大提高了修剪和阈值处理操作的效率，对于检测嵌入背景噪声中的缺陷回波非常有用。通过实验验证了所提方法的性能，并与小波变换信号处理器进行了比较。本文给出了检测受加性白高斯噪声或相关噪声污染的超声缺陷回波的实验结果。
