TY - JOUR
T1 - The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising
AU - Censor , Yair
AU - Gibali , Aviv
AU - Lenzen , Frank
AU - Schnörr , Christoph
JO - Journal of Computational Mathematics
VL - 6
SP - 610
EP - 625
PY - 2016
DA - 2016/12
SN - 34
DO - http://doi.org/10.4208/jcm.1606-m2016-0581
UR - https://global-sci.org/intro/article_detail/jcm/9816.html
KW - Implicit convex feasibility, Split feasibility, projection methods, Variable sets, Proximity function, Image denoising.
AB - <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>