1 research outputs found
Fast Stochastic Hierarchical Bayesian MAP for Tomographic Imaging
Any image recovery algorithm attempts to achieve the highest quality
reconstruction in a timely manner. The former can be achieved in several ways,
among which are by incorporating Bayesian priors that exploit natural image
tendencies to cue in on relevant phenomena. The Hierarchical Bayesian MAP
(HB-MAP) is one such approach which is known to produce compelling results
albeit at a substantial computational cost. We look to provide further analysis
and insights into what makes the HB-MAP work. While retaining the proficient
nature of HB-MAP's Type-I estimation, we propose a stochastic
approximation-based approach to Type-II estimation. The resulting algorithm,
fast stochastic HB-MAP (fsHBMAP), takes dramatically fewer operations while
retaining high reconstruction quality. We employ our fsHBMAP scheme towards the
problem of tomographic imaging and demonstrate that fsHBMAP furnishes promising
results when compared to many competing methods.Comment: 5 Pages, 4 Figures, Conference (Accepted to Asilomar 2017