Blind Deconvolution of Ultrasonic Signals Using High-Order Spectral Analysis and Wavelets

TL;DR

This paper introduces a wavelet-regularized Wiener deconvolution method to enhance ultrasonic defect detection, improving resolution and signal-to-noise ratio by estimating the reflectivity function more effectively.

Contribution

It presents a novel regularization approach combining wavelet shrinkage with Wiener filtering for ultrasonic signal deconvolution.

Findings

Enhanced axial resolution in ultrasonic signals.

Improved signal-to-noise ratio.

Effective estimation of reflectivity function.

Abstract

Defect detection by ultrasonic method is limited by the pulse width. Resolution can be improved through a deconvolution process with a priori information of the pulse or by its estimation. In this paper a regularization of the Wiener filter using wavelet shrinkage is presented for the estimation of the reflectivity function. The final result shows an improved signal to noise ratio with better axial resolution.