Variational methods for compressive sampling

摘要

We consider two natural extensions of the standard l_1 minimization framework for compressive sampling. The two methods, one based on penalizing second-order derivatives and one based on the redundant wavelet transform, can also be viewed as variational methods which extend the basic total-variation recovery program. A numerical example illustrates that these methods tend to produce smoother images than the standard recovery programs.