  We consider the problem of recovering a signal from nonlinear
transformations, under convex constraints modeling a priori information.
Standard feasibility and optimization methods are ill-suited to tackle this
problem due to the nonlinearities. We show that, in many common applications,
the transformation model can be associated with fixed point equations involving
firmly nonexpansive operators. In turn, the recovery problem is reduced to a
tractable common fixed point formulation, which is solved efficiently by a
provably convergent, block-iterative algorithm. Applications to signal and
image recovery are demonstrated. Inconsistent problems are also addressed.
