Abstract
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) |
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Title of host publication | Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 |
Editors | Michael B. Matthews |
Publisher | IEEE Computer Society |
Pages | 1021-1025 |
Number of pages | 5 |
ISBN (Electronic) | 9781728143002 |
DOIs | |
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 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2019-November |
ISSN (Print) | 1058-6393 |
Conference
Conference | 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 11/3/19 → 11/6/19 |
Bibliographical note
Funding Information:The authors graciously acknowledge support from the DARPA REVEAL program, Contract No. HR0011-16-C-0024.
Publisher Copyright:
© 2019 IEEE.
Keywords
- Cramer-Rao bound
- Hammersley-Chapman-Robbins bound
- Plenoptic Imaging