Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation
This work addresses the challenge of accurately analyzing ultrasonic signals for nondestructive evaluation, though it appears incremental as it adapts an existing framework to a specific application.
The paper tackled the problem of reconstructing the reflectivity function of a medium in nondestructive evaluation using a blind deconvolution framework, resulting in stable pulse estimates and improved signal-to-noise ratio and axial resolution in ultrasonic signals.
In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener filter is used to obtain the ultrasonic reflectivity function through wavelet-based models. A new approach to the parameter estimation of the inverse filtering step is proposed in the nondestructive evaluation field, which is based on the theory of Fourier-Wavelet regularized deconvolution (ForWaRD). This new approach can be viewed as a solution to the open problem of adaptation of the ForWaRD framework to perform the convolution kernel estimation and deconvolution interdependently. The results indicate stable solutions of the estimated pulse and an improvement in the radio-frequency (RF) signal taking into account its signal-to-noise ratio (SNR) and axial resolution. Simulations and experiments showed that the proposed approach can provide robust and optimal estimates of the reflectivity function.