  In this paper we present a blind deconvolution scheme based on statistical
wavelet estimation. We assume no prior knowledge of the wavelet, and do not
select a reflector from the signal. Instead, the wavelet (ultrasound pulse) is
statistically estimated from the signal itself by a kurtosis-based metric. This
wavelet is then used to deconvolve the RF (radiofrequency) signal through
Wiener filtering, and the resultant zero phase trace is subjected to spectral
broadening by Autoregressive Spectral Extrapolation (ASE). These steps increase
the time resolution of diffraction techniques. Results on synthetic and real
cases show the robustness of the proposed method.
