  In this work, we consider a class of differentiable criteria for sparse image
computing problems, where a nonconvex regularization is applied to an arbitrary
linear transform of the target image. As special cases, it includes
edge-preserving measures or frame-analysis potentials commonly used in image
processing. As shown by our asymptotic results, the l2-l0 penalties we consider
may be employed to provide approximate solutions to l0-penalized optimization
problems. One of the advantages of the proposed approach is that it allows us
to derive an efficient Majorize-Minimize subspace algorithm. The convergence of
the algorithm is investigated by using recent results in nonconvex
optimization. The fast convergence properties of the proposed optimization
method are illustrated through image processing examples. In particular, its
effectiveness is demonstrated on several data recovery problems.
