780 research outputs found
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
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
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
Parabolic Molecules
Anisotropic decompositions using representation systems based on parabolic
scaling such as curvelets or shearlets have recently attracted significantly
increased attention due to the fact that they were shown to provide optimally
sparse approximations of functions exhibiting singularities on lower
dimensional embedded manifolds. The literature now contains various direct
proofs of this fact and of related sparse approximation results. However, it
seems quite cumbersome to prove such a canon of results for each system
separately, while many of the systems exhibit certain similarities.
In this paper, with the introduction of the notion of {\em parabolic
molecules}, we aim to provide a comprehensive framework which includes
customarily employed representation systems based on parabolic scaling such as
curvelets and shearlets. It is shown that pairs of parabolic molecules have the
fundamental property to be almost orthogonal in a particular sense. This result
is then applied to analyze parabolic molecules with respect to their ability to
sparsely approximate data governed by anisotropic features. For this, the
concept of {\em sparsity equivalence} is introduced which is shown to allow the
identification of a large class of parabolic molecules providing the same
sparse approximation results as curvelets and shearlets. Finally, as another
application, smoothness spaces associated with parabolic molecules are
introduced providing a general theoretical approach which even leads to novel
results for, for instance, compactly supported shearlets
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
Asymptotic Analysis of Inpainting via Universal Shearlet Systems
Recently introduced inpainting algorithms using a combination of applied
harmonic analysis and compressed sensing have turned out to be very successful.
One key ingredient is a carefully chosen representation system which provides
(optimally) sparse approximations of the original image. Due to the common
assumption that images are typically governed by anisotropic features,
directional representation systems have often been utilized. One prominent
example of this class are shearlets, which have the additional benefitallowing
faithful implementations. Numerical results show that shearlets significantly
outperform wavelets in inpainting tasks. One of those software packages,
www.shearlab.org, even offers the flexibility of usingdifferent parameter for
each scale, which is not yet covered by shearlet theory.
In this paper, we first introduce universal shearlet systems which are
associated with an arbitrary scaling sequence, thereby modeling the previously
mentioned flexibility. In addition, this novel construction allows for a smooth
transition between wavelets and shearlets and therefore enables us to analyze
them in a uniform fashion. For a large class of such scaling sequences, we
first prove that the associated universal shearlet systems form band-limited
Parseval frames for consisting of Schwartz functions.
Secondly, we analyze the performance for inpainting of this class of universal
shearlet systems within a distributional model situation using an
-analysis minimization algorithm for reconstruction. Our main result in
this part states that, provided the scaling sequence is comparable to the size
of the (scale-dependent) gap, nearly-perfect inpainting is achieved at
sufficiently fine scales
Scale Invariant Interest Points with Shearlets
Shearlets are a relatively new directional multi-scale framework for signal
analysis, which have been shown effective to enhance signal discontinuities
such as edges and corners at multiple scales. In this work we address the
problem of detecting and describing blob-like features in the shearlets
framework. We derive a measure which is very effective for blob detection and
closely related to the Laplacian of Gaussian. We demonstrate the measure
satisfies the perfect scale invariance property in the continuous case. In the
discrete setting, we derive algorithms for blob detection and keypoint
description. Finally, we provide qualitative justifications of our findings as
well as a quantitative evaluation on benchmark data. We also report an
experimental evidence that our method is very suitable to deal with compressed
and noisy images, thanks to the sparsity property of shearlets
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