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Statistical X-ray tomography using empirical Besov priors

Simopekka Vanska, Matti Lassas, Samuli Siltanen

Abstract


Wavelet-based Besov space prior models for X-ray tomography are studied using the empirical Bayes approach. The hyperparameters for the prior models are estimated from statistical properties of the wavelet coefficients of measured X-ray projection images (which are related to the smoothness of the attenuation coefficient). Various statistical models for the wavelet coefficients are studied. Experiments using measured in vitro data suggest that the hyperparameters can be estimated with a simple method, leading to automated choice of the prior parameters and improved tomographic reconstruction.

Keywords


tomography, wavelets, Besov space, prior

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