  Recovering nonlinearly degraded signal in the presence of noise is a
challenging problem. In this work, this problem is tackled by minimizing the
sum of a non convex least-squares fit criterion and a penalty term. We assume
that the nonlinearity of the model can be accounted for by a rational function.
In addition, we suppose that the signal to be sought is sparse and a rational
approximation of the $\ell_0$ pseudo-norm thus constitutes a suitable
penalization. The resulting composite cost function belongs to the broad class
of semi-algebraic functions. To find a globally optimal solution to such an
optimization problem, it can be transformed into a generalized moment problem,
for which a hierarchy of semidefinite programming relaxations can be built.
Global optimality comes at the expense of an increased dimension and, to
overcome computational limitations concerning the number of involved variables,
the structure of the problem has to be carefully addressed. A situation of
practical interest is when the nonlinear model consists of a convolutive
transform followed by a componentwise nonlinear rational saturation. We then
propose to use a sparse relaxation able to deal with up to several hundreds of
optimized variables. In contrast with the naive approach consisting of
linearizing the model, our experiments show that the proposed approach offers
good performance.
