@Article{JCM-34-6,
author = {Yair, Censor and Gibali, Aviv and Lenzen, Frank and Schnörr, Christoph},
title = {The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising},
journal = {Journal of Computational Mathematics},
year = {2016},
volume = {34},
number = {6},
pages = {610--625},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.1606-m2016-0581},
url = {https://global-sci.com/article/84575/the-implicit-convex-feasibility-problem-and-its-application-to-adaptive-image-denoising}
}