Image restoration by convex projections in the presence of noise

In this paper we investigate how the method of convex projections for image restoration behaves in the presence of noise. We also introduce and test a new noise-smoothing procedure in which the restored image is forced to lie within a certain L2 distance of the noisy data. We show that, in the presence of noise, restoration by convex projections is superior to the Gerchberg-Papoulis method.