Image reconstruction from coded data: I. reconstruction algorithms and experimental results

Warren E. Smith, Richard G. Paxman, Harrison H. Barrett

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Two algorithms have been developed for reconstructing objects from their coded images and a priori knowledge of the object class. Reconstructions from both algorithms are presented, but the results appear to be largely independent of the algorithm used. One of the algorithms, a Monte Carlo approach, is used to investigate the quality of the reconstruction of two- and three-dimensional objects from simulated coded-image data with respect to viewing geometry and multiplexing (mixing) of the data. The cases examined include reconstructions from data with and without signal-dependent photon noise. It is found that reconstructing from multiplexed data is not so serious a problem as reconstructing from data obtained with a limited viewing angle. Also, when photon noise is included in the data, reconstructions obtained from multiplexed data are better than those obtained from unmultiplexed data because of the higher photon count made available by multiplexing. It appears that the fidelity of a reconstruction depends much more strongly on the design of the data-taking system (the coded apertures) than on the reconstruction algorithm.

Original languageEnglish (US)
Pages (from-to)491-500
Number of pages10
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number4
StatePublished - Apr 1 1985

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Image reconstruction from coded data: I. reconstruction algorithms and experimental results'. Together they form a unique fingerprint.

Cite this