Wavelet‐based blind deconvolution of near‐field ultrasound scans

Abstract

A wavelet-based technique for blind deconvolution and denoising of ultrasound scans is introduced. The target application is near-field ultrasound imaging for non-destructive testing. Existing blind deconvolution techniques for ultrasound such as cepstrum-based methods and the work of Adam and Michailovich – based on discrete wavelet transform (DWT) shrinkage of the log-spectrum – estimate the pulse by exploiting the pulse log-spectrum smoothness relative to the material reflectivity function. In the proposed technique, the log-spectrum is localised with respect to time as the continuous wavelet transform (CWT) log-scalogram to deal with the non-stationarity of the near-field ultrasound signals in both the pulse estimation and deconvolution. The pulse is estimated in the wavelet domain via DWT shrinkage of the log-scalogram and is deconvolved by wavelet-domain Wiener filtering. Extensions of the proposed technique include: using separate CWT domains for estimation and deconvolution, as inspired by the WienerChop denoising method; and training the algorithm parameters on a subset of scans.