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Proceedings Paper

Robust Object Reconstruction From Noisy Observations
Author(s): G. Sundaramoorthy; M. R. Raghuveer; S. A. Dianat
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Paper Abstract

The problem of reconstructing a moving object from multiple snapshots contaminated by noise arises in many imaging applications. Many techniques have been proposed for noise elimination that rely either on measurements of the autocorrelation and/or power spectrum of the observations, or on the assumption that the additive noise is white. The power spectrum is affected by additive noise, and in many situations, the noise is spatially or temporally correlated. The above techniques are sensitive to deviations from assumptions. The bispectrum is identically zero for random processes with symmetric distributions regardless of spatial or temporal correlations. This property along with its ability to retain phase and magnitude information, have led researchers to propose bispectral techniques for estimating parameters of random signals in noise. The bispectrum is also insensitive to translational motion. If these properties are to be taken advantage of to solve the moving object (deterministic signal in noise) reconstruction problem, it is necessary to obtain good estimates of the bispectrum of the object from the noisy observations. In order to do this it is necessary to restrict bispectrum estimation to a certain region of the frequency plane. A consequence of this is that several techniques proposed for bispectral analysis of random signals cannot be used. The paper develops new approaches which enable signal reconstruction from bispectrum measurements made over the restricted region. Simulations of application of these techniques to moving object reconstruction, data transmission over channels with jitter and noise, and image restoration, show that they are more robust with respect to the statistics of the contaminating noise than methods based on autocorrelation.

Paper Details

Date Published: 1 November 1989
PDF: 11 pages
Proc. SPIE 1199, Visual Communications and Image Processing IV, (1 November 1989); doi: 10.1117/12.970023
Show Author Affiliations
G. Sundaramoorthy, Rochester Institute of Technology (United States)
M. R. Raghuveer, Rochester Institute of Technology (United States)
S. A. Dianat, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1199:
Visual Communications and Image Processing IV
William A. Pearlman, Editor(s)

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