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In this paper we present a blind deconvolution scheme based on statisticalwavelet estimation. We assume no prior knowledge of the wavelet, and do notselect a reflector from the signal. Instead, the wavelet (ultrasound pulse) isstatistically estimated from the signal itself by a kurtosis-based metric. Thiswavelet is then used to deconvolve the RF (radiofrequency) signal throughWiener filtering, and the resultant zero phase trace is subjected to spectralbroadening by Autoregressive Spectral Extrapolation (ASE). These steps increasethe time resolution of diffraction techniques. Results on synthetic and realcases show the robustness of the proposed method.

Improving Time Estimation by Blind Deconvolution: with Applications to TOFD and Backscatter Sizing