We present a framework to compute lower bounds for parameter estimation from noisy plenoptic observations. Our particular focus is on indirect imaging problems, where the observations do not contain line-of-sight information about the parameter(s) of interest. Using computer graphics rendering software to synthesize the (often complicated) dependence among parameter(s) of interest and observations, we numerically evaluate Barankin bounds for these tasks. We demonstrate our results on some canonical example scenes.

This presentation is part of Minisymposium “MS60 - Computational and Compressive Imaging Technologies and Applications (3 parts)”
organized by: Robert Muise (Lockheed Martin) , Richard Baraniuk (Rice University) .

Authors:

Jarvis Haupt (University of Minnesota)

Abhinav V. Sambasivan (University of Minnesota)

Richard Paxman (MDA Information Systems, LLC)

Keywords:

barankin bound, computer graphics, estimation theory, plenoptic imaging