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Proceedings Paper

Solving non-negative linear inverse problems with the NeAREst method
Author(s): Xiaobai Sun; Nikos P. Pitsianis
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Paper Abstract

This paper introduces the theoretical development of a numerical method, named NeAREst, for solving non-negative linear inverse problems, which arise often from physical or probabilistic models, especially, in image estimation with limited and indirect measurements. The Richardson-Lucy (RL) iteration is omnipresent in conventional methods that are based on probabilistic assumptions, arguments and techniques. Without resorting to probabilistic assumptions, NeAREst retains many appealing properties of the RL iteration by utilizing it as the substrate process and provides much needed mechanisms for acceleration as well as for selection of a target solution when many admissible ones exist.

Paper Details

Date Published: 3 September 2008
PDF: 10 pages
Proc. SPIE 7074, Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII, 707402 (3 September 2008); doi: 10.1117/12.795479
Show Author Affiliations
Xiaobai Sun, Duke Univ. (United States)
Nikos P. Pitsianis, Duke Univ. (United States)
Aristotle Univ. (Greece)

Published in SPIE Proceedings Vol. 7074:
Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII
Franklin T. Luk, Editor(s)

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