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Proceedings Paper

Enforcing nonnegativity in image reconstruction algorithms
Author(s): James G. Nagy; Zdenek Strakos
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Paper Abstract

In image restoration and reconstruction applications, unconstrained Krylov subspace methods represent an attractive approach for computing approximate solutions. They are fast, but unfortunately they do not produce approximate solutions preserving nonnegativity. As a consequence the error of the computed approximate solution can be large. Enforcing a nonnegativity constraint can produce much more accurate approximate solutions, but can also be computationally expensive. This paper considers a nonnegativity constrained minimization algorithm which represents a variant of an algorithm proposed by Kaufman. Numerical experiments show that the algorithm can be more accurate and computationally competitive with unconstrained Krylov subspace methods.

Paper Details

Date Published: 4 October 2000
PDF: 9 pages
Proc. SPIE 4121, Mathematical Modeling, Estimation, and Imaging, (4 October 2000); doi: 10.1117/12.402439
Show Author Affiliations
James G. Nagy, Emory Univ. (United States)
Zdenek Strakos, Institute of Computer Science (Czech Republic)

Published in SPIE Proceedings Vol. 4121:
Mathematical Modeling, Estimation, and Imaging
David C. Wilson; Hemant D. Tagare; Fred L. Bookstein; Francoise J. Preteux; Edward R. Dougherty, Editor(s)

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