Year: 2016
Author: Yair Censor, Aviv Gibali, Frank Lenzen, Christoph Schnörr
Journal of Computational Mathematics, Vol. 34 (2016), Iss. 6 : pp. 610–625
Abstract
The implicit convex feasibility problem attempts to find a point in the intersection of a finite family of convex sets, some of which are not explicitly determined but may vary. We develop simultaneous and sequential projection methods capable of handling such problems and demonstrate their applicability to image denoising in a specific medical imaging situation. By allowing the variable sets to undergo scaling, shifting and rotation, this work generalizes previous results wherein the implicit convex feasibility problem was used for cooperative wireless sensor network positioning where sets are balls and their centers were implicit.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.1606-m2016-0581
Journal of Computational Mathematics, Vol. 34 (2016), Iss. 6 : pp. 610–625
Published online: 2016-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 16
Keywords: Implicit convex feasibility Split feasibility projection methods Variable sets Proximity function Image denoising.