Optimally sparse approximations of 3D functions by compactly supported shearlet frames

We study efficient and reliable methods of capturing and sparsely representing anisotropic structures in 3D data. As a model class for multidimensional data with anisotropic features, we introduce generalized three-dimensional cartoon-like images. This function class will have two smoothness parameters: one parameter βcontrolling classical smoothness and one parameter αcontrolling anisotropic smoothness. The class then consists of piecewise C^β-smooth functions with discontinuities on a piecewise C^α-smooth surface. We introduce a pyramid-adapted, hybrid shearlet system for the three-dimensional setting and construct frames for L^2(R^3) with this particular shearlet structure. For the smoothness range 1<α=< β=< 2 we show that pyramid-adapted shearlet systems provide a nearly optimally sparse approximation rate within the generalized cartoon-like image model class measured by means of non-linear N-term approximations.

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