  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.
