A Renderer-Enabled Framework for Computing Parameter Estimation Lower Bounds in Plenoptic Imaging Systems
This addresses the challenge of determining fundamental estimation limits in complex imaging systems for researchers in computational imaging and signal processing, but it is incremental as it builds on existing bounds and rendering techniques.
This work tackles the problem of assessing information-theoretic limits for scene parameter estimation in plenoptic imaging systems by presenting a framework to compute lower bounds on estimation error, particularly for passive indirect imaging. The results show that the computed lower bounds are indicative of true fundamental limits in representative scenarios, as validated through comparisons with Maximum Likelihood Estimator performance.
This work focuses on assessing the information-theoretic limits of scene parameter estimation in plenoptic imaging systems. A general framework to compute lower bounds on the parameter estimation error from noisy plenoptic observations is presented, with a particular focus on passive 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, i.e. the forward model, the proposed framework evaluates the Hammersley-Chapman-Robbins bound to establish lower bounds on the variance of any unbiased estimator of the unknown parameters. The effects of inexact rendering of the true forward model on the computed lower bounds are also analyzed, both theoretically and via simulations. Experimental evaluations compare the computed lower bounds with the performance of the Maximum Likelihood Estimator on a canonical object localization problem, showing that the lower bounds computed via the framework proposed here are indicative of the true underlying fundamental limits in several nominally representative scenarios.