This work focuses on assessing the information-theoretic limits of parameter estimation in plenoptic imaging systems, which are capable of providing substantially more information about a given scene than conventional cameras. We present a framework to compute lower bounds for parameter estimation from noisy plenoptic observations, and our particular focus is on indirect imaging problems, where the observations do not contain line-of-sight (LOS) 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 the Hammersley-Chapman-Robbins bound to establish fundamental lower limits on the variance of any unbiased estimators of the unknown parameters. We demonstrate the utility of our proposed framework on a few canonical estimation tasks.
|Original language||English (US)|
|Title of host publication||Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019|
|Editors||Michael B. Matthews|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - Nov 2019|
|Event||53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States|
Duration: Nov 3 2019 → Nov 6 2019
|Name||Conference Record - Asilomar Conference on Signals, Systems and Computers|
|Conference||53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019|
|Period||11/3/19 → 11/6/19|
Bibliographical noteFunding Information:
The authors graciously acknowledge support from the DARPA REVEAL program, Contract No. HR0011-16-C-0024.
© 2019 IEEE.
Copyright 2020 Elsevier B.V., All rights reserved.
- Cramer-Rao bound
- Hammersley-Chapman-Robbins bound
- Plenoptic Imaging