8,540 research outputs found
Measure Functions for Frames
This paper addresses the natural question: ``How should frames be compared?''
We answer this question by quantifying the overcompleteness of all frames with
the same index set. We introduce the concept of a frame measure function: a
function which maps each frame to a continuous function. The comparison of
these functions induces an equivalence and partial order that allows for a
meaningful comparison of frames indexed by the same set. We define the
ultrafilter measure function, an explicit frame measure function that we show
is contained both algebraically and topologically inside all frame measure
functions. We explore additional properties of frame measure functions, showing
that they are additive on a large class of supersets-- those that come from so
called non-expansive frames. We apply our results to the Gabor setting,
computing the frame measure function of Gabor frames and establishing a new
result about supersets of Gabor frames.Comment: 54 pages, 1 figure; fixed typos, reformatted reference
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
Gabor Shearlets
In this paper, we introduce Gabor shearlets, a variant of shearlet systems,
which are based on a different group representation than previous shearlet
constructions: they combine elements from Gabor and wavelet frames in their
construction. As a consequence, they can be implemented with standard filters
from wavelet theory in combination with standard Gabor windows. Unlike the
usual shearlets, the new construction can achieve a redundancy as close to one
as desired. Our construction follows the general strategy for shearlets. First
we define group-based Gabor shearlets and then modify them to a cone-adapted
version. In combination with Meyer filters, the cone-adapted Gabor shearlets
constitute a tight frame and provide low-redundancy sparse approximations of
the common model class of anisotropic features which are cartoon-like
functions.Comment: 24 pages, AMS LaTeX, 4 figure
ShearLab 3D: Faithful Digital Shearlet Transforms based on Compactly Supported Shearlets
Wavelets and their associated transforms are highly efficient when
approximating and analyzing one-dimensional signals. However, multivariate
signals such as images or videos typically exhibit curvilinear singularities,
which wavelets are provably deficient of sparsely approximating and also of
analyzing in the sense of, for instance, detecting their direction. Shearlets
are a directional representation system extending the wavelet framework, which
overcomes those deficiencies. Similar to wavelets, shearlets allow a faithful
implementation and fast associated transforms. In this paper, we will introduce
a comprehensive carefully documented software package coined ShearLab 3D
(www.ShearLab.org) and discuss its algorithmic details. This package provides
MATLAB code for a novel faithful algorithmic realization of the 2D and 3D
shearlet transform (and their inverses) associated with compactly supported
universal shearlet systems incorporating the option of using CUDA. We will
present extensive numerical experiments in 2D and 3D concerning denoising,
inpainting, and feature extraction, comparing the performance of ShearLab 3D
with similar transform-based algorithms such as curvelets, contourlets, or
surfacelets. In the spirit of reproducible reseaerch, all scripts are
accessible on www.ShearLab.org.Comment: There is another shearlet software package
(http://www.mathematik.uni-kl.de/imagepro/members/haeuser/ffst/) by S.
H\"auser and G. Steidl. We will include this in a revisio
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