基于提升小波变换和AdaBoost的超声信号消噪技术

Denoising Techniques for Ultrasonic Signals Based on Lifting Wavelet Transform and AdaBoost

为了提高超声无损检测（UNDT）和无损评价（UNDE）中基础数据的信噪比（SNR），提出了一种基于提升小波变换和AdaBoost模式识别理论的超声信号消噪技术。该技术在研究材料内部散射体引起的结构噪声产生机理，以及分析传统裂谱分析算法局限性的基础上，利用提升小波变换将原始超声检测信号分解到小波空间后，通过采用AdaBoost算法构成的信噪分离器对信号和噪声进行识别、分离来消除噪声，得到高信噪比的超声回波信号。实验结果表明，与传统裂谱分析算法相比，该技术提高了消噪性能的稳定性，增强了湮没材料内部各种散射体散射中的缺陷回波信号能力。

In order to enhance the signal to noise ratio （SNR） of fundamental ultrasonic echo signals for ultrasonic nondestructive testing （UNDT） and ultrasonic nondestructive evaluation （UNDE）, an improved technique to suppress structural noises of ultrasonic signals on the basis of lifting wavelet transform and AdaBoost pattern recognition theory was presented. After the formation mechanism of structural noises was studied and the shortcomings of classical split spectrum processing （SSP） algorithm were analyzed, the fundamental ultrasonic signals were decomposed into wavelet domain by lifting wavelet transform. A signal and noise separator based on AdaBoost was used to distinguish the target signals from the noises in wavelet domain, and the target signals were reconstructed to realize the aim of enhancing SNR by removing noises. The experiment results indicate that the presented technique has high performance reliability and can improve the SNR enhancing ability for ultrasonic target echo signals contaminated by structural noises compared with the classical SSP algorithm.