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Proceedings Paper

Iterative reconstruction of a moving object from image sequences corrupted by strong structured noise
Author(s): Ikram E. Abdou
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Paper Abstract

The reconstruction of a moving object from image sequences corrupted by strong structured noise is one of many signal processing problems that require the estimation of the components of a composite field from its sum. In these problems, the composite field is the superposition of one signal that corresponds to the object of interest, other signals that represent clutter, the effect of the transmission medium on the received signal, and signal noise. The traditional solution to these problems is based on modeling the distortion as either a periodic deterministic signal (such as a sine wave) or a random signal (such as Gaussian noise) and using this model to reconstruct the signal of interest. In this paper, we develop a more general algorithm that can be used to reconstruct the relevant object signal from a sequence of frames, with little restriction on the distortion patterns. We begin by developing an analytical model for the reconstruction of composite signals and show that a direct exact solution for this problem does not exist. Next, we apply an iterative procedure based on Kaczmarz's method of image restoration. We show that when the signal of interest has limited support (i.e., is nonzero over a window that is smaller than the image size), an exact restoration is feasible. For this case, we study the effect of the number of frames and the relaxation coefficient used in the iterative algorithm on its rate of convergence. Finally, we discuss other applications of this technique such as the restoration of composite vector fields.

Paper Details

Date Published: 21 September 1994
PDF: 14 pages
Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); doi: 10.1117/12.186545
Show Author Affiliations
Ikram E. Abdou, SRI International (United States)

Published in SPIE Proceedings Vol. 2298:
Applications of Digital Image Processing XVII
Andrew G. Tescher, Editor(s)

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