13,117 research outputs found
Optimally Sparse Frames
Frames have established themselves as a means to derive redundant, yet stable
decompositions of a signal for analysis or transmission, while also promoting
sparse expansions. However, when the signal dimension is large, the computation
of the frame measurements of a signal typically requires a large number of
additions and multiplications, and this makes a frame decomposition intractable
in applications with limited computing budget. To address this problem, in this
paper, we focus on frames in finite-dimensional Hilbert spaces and introduce
sparsity for such frames as a new paradigm. In our terminology, a sparse frame
is a frame whose elements have a sparse representation in an orthonormal basis,
thereby enabling low-complexity frame decompositions. To introduce a precise
meaning of optimality, we take the sum of the numbers of vectors needed of this
orthonormal basis when expanding each frame vector as sparsity measure. We then
analyze the recently introduced algorithm Spectral Tetris for construction of
unit norm tight frames and prove that the tight frames generated by this
algorithm are in fact optimally sparse with respect to the standard unit vector
basis. Finally, we show that even the generalization of Spectral Tetris for the
construction of unit norm frames associated with a given frame operator
produces optimally sparse frames
Shearlets and Optimally Sparse Approximations
Multivariate functions are typically governed by anisotropic features such as
edges in images or shock fronts in solutions of transport-dominated equations.
One major goal both for the purpose of compression as well as for an efficient
analysis is the provision of optimally sparse approximations of such functions.
Recently, cartoon-like images were introduced in 2D and 3D as a suitable model
class, and approximation properties were measured by considering the decay rate
of the error of the best -term approximation. Shearlet systems are to
date the only representation system, which provide optimally sparse
approximations of this model class in 2D as well as 3D. Even more, in contrast
to all other directional representation systems, a theory for compactly
supported shearlet frames was derived which moreover also satisfy this
optimality benchmark. This chapter shall serve as an introduction to and a
survey about sparse approximations of cartoon-like images by band-limited and
also compactly supported shearlet frames as well as a reference for the
state-of-the-art of this research field.Comment: in "Shearlets: Multiscale Analysis for Multivariate Data",
Birkh\"auser-Springe
Compactly Supported Shearlets are Optimally Sparse
Cartoon-like images, i.e., C^2 functions which are smooth apart from a C^2
discontinuity curve, have by now become a standard model for measuring sparse
(non-linear) approximation properties of directional representation systems. It
was already shown that curvelets, contourlets, as well as shearlets do exhibit
(almost) optimally sparse approximation within this model. However, all those
results are only applicable to band-limited generators, whereas, in particular,
spatially compactly supported generators are of uttermost importance for
applications.
In this paper, we now present the first complete proof of (almost) optimally
sparse approximations of cartoon-like images by using a particular class of
directional representation systems, which indeed consists of compactly
supported elements. This class will be chosen as a subset of shearlet frames --
not necessarily required to be tight -- with shearlet generators having compact
support and satisfying some weak moment conditions
Gabor representations of evolution operators
We perform a time-frequency analysis of Fourier multipliers and, more
generally, pseudodifferential operators with symbols of Gevrey, analytic and
ultra-analytic regularity. As an application we show that Gabor frames, which
provide optimally sparse decompositions for Schroedinger-type propagators,
reveal to be an even more efficient tool for representing solutions to a wide
class of evolution operators with constant coefficients, including weakly
hyperbolic and parabolic-type operators. Besides the class of operators, the
main novelty of the paper is the proof of super-exponential (as opposite to
super-polynomial) off-diagonal decay for the Gabor matrix representation.Comment: 26 page
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 \beta controlling classical smoothness and
one parameter \alpha controlling anisotropic smoothness. The class then
consists of piecewise C^\beta-smooth functions with discontinuities on a
piecewise C^\alpha-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<\alpha =< \beta =< 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.Comment: 56 pages, 6 figure
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