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Empirical Bayesian Regularization of the Inverse Problem for Diffuse Optical Tomography with Multiple Priors

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Abstract

Image reconstruction of diffuse optical data is underdetermined inverse problem requiring regularization to obtain accurate images. We describe the application of empirical Bayesian methods to obtain optimal regularization levels based on maximizing the log-likelihood function.

© 2010 Optical Society of America

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