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Any image recovery algorithm attempts to achieve the highest qualityreconstruction in a timely manner. The former can be achieved in several ways,among which are by incorporating Bayesian priors that exploit natural imagetendencies to cue in on relevant phenomena. The Hierarchical Bayesian MAP(HB-MAP) is one such approach which is known to produce compelling resultsalbeit at a substantial computational cost. We look to provide further analysisand insights into what makes the HB-MAP work. While retaining the proficientnature of HB-MAP's Type-I estimation, we propose a stochasticapproximation-based approach to Type-II estimation. The resulting algorithm,fast stochastic HB-MAP (fsHBMAP), takes dramatically fewer operations whileretaining high reconstruction quality. We employ our fsHBMAP scheme towards theproblem of tomographic imaging and demonstrate that fsHBMAP furnishes promisingresults when compared to many competing methods.

Fast Stochastic Hierarchical Bayesian MAP for Tomographic Imaging