Optical diffraction tomography within a variational Bayesian framework
Abstract
Optical diffraction tomography techniques aim to find an image of an unknown object (e.g. a map of its refraction index) using measurements of the scattered field that results from its interaction with a known interrogating wave. We address this issue as a non-linear inverse scattering problem. The forward model is based upon domain integral representations of the electric field whose discrete counterparts are obtained by means of a method of moments. The inverse problem is tackled in a Bayesian estimation framework involving a hierarchical prior model that accounts for the piece-wise homogeneity of the object. A joint unsupervised estimation approach is adopted to estimate the induced currents, the contrast and all the other parameters introduced in the prior model. As an analytic expression for the joint Maximum A Posteriori (MAP) and Posterior Mean (PM) estimators is hard to obtain, a tractable approximation of the latter is proposed. This approximation is based upon a variational Bayesian technique and consists in the best separable distribution that approximates the true posterior distribution in the Kullback-Leibler sense. This leads to an implicit parametric optimization scheme which is solved iteratively.