The Curvelet Representation of Wave Propagators is Optimally Sparse
  This paper argues that curvelets provide a powerful tool for representing very general linear symmetric systems of hyperbolic differential equations. Curvelets are a recently developed multiscale system in which the elements are highly anisotropic at fine scales, with effective support shaped according to the parabolic scaling principle width ~ length^2 at fine scales. We prove that for a wide class of linear hyperbolic differential equations, the curvelet representation of the solution operator is both optimally sparse and well organized. It is sparse in the sense that the matrix entries decay nearly exponentially fast (i.e. faster than any negative polynomial), and well-organized in the sense that the very few nonnegligible entries occur near a shifted diagonal. Indeed, we actually show that the action of the wave-group on a curvelet is well-approximated by simply translating the center of the curvelet along the Hamiltonian flow--hence the diagonal shift in the curvelet epresentation. A physical interpretation of this result is that curvelets may be viewed as coherent waveforms with enough frequency localization so that they behave like waves but at the same time, with enough spatial localization so that they simultaneously behave like particles. 
Emmanuel J. Candes and Laurent Demanet