The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising

The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising

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.

Author Details

Yair Censor

Aviv Gibali

Frank Lenzen

Christoph Schnörr

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