Open AccessPerformance of 3D integral imaging with position uncertainty
Author(s) -
Behnoosh Tavakoli,
Mehdi Daneshpanah,
Bahram Javidi,
Edward Watson
Publication year - 2007
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.15.011889
Subject(s) - integral imaging , position (finance) , iterative reconstruction , metric (unit) , optics , sensitivity (control systems) , computer science , perspective (graphical) , mean squared error , aperture (computer memory) , image quality , spatial frequency , computer vision , inverse problem , artificial intelligence , image (mathematics) , mathematics , physics , acoustics , statistics , mathematical analysis , operations management , finance , electronic engineering , engineering , economics
We present the theoretical and simulation results on the analysis of Synthetic Aperture Integral Imaging (SAII) technique and its sensitivity to pickup position uncertainty. SAII is a passive three dimensional imaging technique based on multiple image acquisitions with different perspective of the scene under incoherent or natural illumination. In practical SAII applications, there is always an uncertainty associated with the position at which each sensor captures the elemental image. We present a theoretical analysis that quantifies image degradation in terms of Mean Square Error (MSE) metric. Simulation results are also presented to identify the parameters affecting the reconstruction degradation and to confirm the analysis. We show that in SAII with a given uncertainty in the sensor locations, the high spatial frequency content of the 3D reconstructed images are most degraded. We also show an inverse relationship between the reconstruction distance and degradation metric. To the best of our knowledge, this is the first time that the effects of sensor position uncertainty on 3D computational reconstruction in synthetic aperture integral imaging systems have been quantitatively analyzed.