access icon free Wavelet-based blind deconvolution of near-field ultrasound scans

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.

Inspec keywords: nondestructive testing; Wiener filters; biomedical ultrasonics; medical image processing; deconvolution; discrete wavelet transforms

Other keywords: nondestructive testing; material reflectivity function; pulse log-spectrum smoothness; wavelet-domain Wiener filtering; DWT shrinkage; near-field ultrasound scans; cepstrum-based methods; pulse estimation; continuous wavelet transform log-scalogram; discrete wavelet transform shrinkage; CWT log-scalogram; WienerChop denoising method; wavelet-based blind deconvolution technique

Subjects: Function theory, analysis; Biology and medical computing; Sonic and ultrasonic radiation (medical uses); Integral transforms; Optical, image and video signal processing; Patient diagnostic methods and instrumentation; Sonic and ultrasonic radiation (biomedical imaging/measurement); Filtering methods in signal processing; Integral transforms; Computer vision and image processing techniques

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