Wavelet-based blind deconvolution and denoising of ultrasound scans for non-destructive test applications
Taylor, Jason Richard Benjamin
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A novel technique for blind deconvolution of ultrasound is introduced. Existing 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 – exploit the smoothness of the pulse log-spectrum relative to the reflectivity function to estimate the pulse. To reduce the effects of non-stationarity in the ultrasound signal on both the pulse estimation and deconvolution, the log-spectrum is time-localized and represented as the Continuous Wavelet Transform (CWT) log-scalogram in the proposed technique. The pulse CWT coefficients are estimated via DWT shrinkage of the log-scalogram and are then deconvolved by wavelet-domain Wiener filtering. Parameters of the technique are found by heuristic optimization on a training set with various quality metrics: entropy, autocorrelation 6-dB width and fractal dimension. The technique is further enhanced by using different CWT wavelets for estimation and deconvolution, similar to the WienerChop method.