5,038 research outputs found
Quantitative analysis of the reconstruction performance of interpolants
The analysis presented provides a quantitative measure of the reconstruction or interpolation performance of linear, shift-invariant interpolants. The performance criterion is the mean square error of the difference between the sampled and reconstructed functions. The analysis is applicable to reconstruction algorithms used in image processing and to many types of splines used in numerical analysis and computer graphics. When formulated in the frequency domain, the mean square error clearly separates the contribution of the interpolation method from the contribution of the sampled data. The equations provide a rational basis for selecting an optimal interpolant; that is, one which minimizes the mean square error. The analysis has been applied to a selection of frequently used data splines and reconstruction algorithms: parametric cubic and quintic Hermite splines, exponential and nu splines (including the special case of the cubic spline), parametric cubic convolution, Keys' fourth-order cubic, and a cubic with a discontinuous first derivative. The emphasis in this paper is on the image-dependent case in which no a priori knowledge of the frequency spectrum of the sampled function is assumed
Exponential Splines of Complex Order
We extend the concept of exponential B-spline to complex orders. This
extension contains as special cases the class of exponential splines and also
the class of polynomial B-splines of complex order. We derive a time domain
representation of a complex exponential B-spline depending on a single
parameter and establish a connection to fractional differential operators
defined on Lizorkin spaces. Moreover, we prove that complex exponential splines
give rise to multiresolution analyses of and define wavelet
bases for
Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems
This paper deals with the resolution of inverse problems in a periodic
setting or, in other terms, the reconstruction of periodic continuous-domain
signals from their noisy measurements. We focus on two reconstruction
paradigms: variational and statistical. In the variational approach, the
reconstructed signal is solution to an optimization problem that establishes a
tradeoff between fidelity to the data and smoothness conditions via a quadratic
regularization associated to a linear operator. In the statistical approach,
the signal is modeled as a stationary random process defined from a Gaussian
white noise and a whitening operator; one then looks for the optimal estimator
in the mean-square sense. We give a generic form of the reconstructed signals
for both approaches, allowing for a rigorous comparison of the two.We fully
characterize the conditions under which the two formulations yield the same
solution, which is a periodic spline in the case of sampling measurements. We
also show that this equivalence between the two approaches remains valid on
simulations for a broad class of problems. This extends the practical range of
applicability of the variational method
Orthonormal bases of regular wavelets in spaces of homogeneous type
Adapting the recently developed randomized dyadic structures, we introduce
the notion of spline function in geometrically doubling quasi-metric spaces.
Such functions have interpolation and reproducing properties as the linear
splines in Euclidean spaces. They also have H\"older regularity. This is used
to build an orthonormal basis of H\"older-continuous wavelets with exponential
decay in any space of homogeneous type. As in the classical theory, wavelet
bases provide a universal Calder\'on reproducing formula to study and develop
function space theory and singular integrals. We discuss the examples of
spaces, BMO and apply this to a proof of the T(1) theorem. As no extra
condition {(like 'reverse doubling', 'small boundary' of balls, etc.)} on the
space of homogeneous type is required, our results extend a long line of works
on the subject.Comment: We have made improvements to section 2 following the referees
suggestions. In particular, it now contains full proof of formerly Theorem
2.7 instead of sending back to earlier works, which makes the construction of
splines self-contained. One reference adde
Kernel Approximation on Manifolds I: Bounding the Lebesgue Constant
The purpose of this paper is to establish that for any compact, connected
C^{\infty} Riemannian manifold there exists a robust family of kernels of
increasing smoothness that are well suited for interpolation. They generate
Lagrange functions that are uniformly bounded and decay away from their center
at an exponential rate. An immediate corollary is that the corresponding
Lebesgue constant will be uniformly bounded with a constant whose only
dependence on the set of data sites is reflected in the mesh ratio, which
measures the uniformity of the data.
The analysis needed for these results was inspired by some fundamental work
of Matveev where the Sobolev decay of Lagrange functions associated with
certain kernels on \Omega \subset R^d was obtained. With a bit more work, one
establishes the following: Lebesgue constants associated with surface splines
and Sobolev splines are uniformly bounded on R^d provided the data sites \Xi
are quasi-uniformly distributed. The non-Euclidean case is more involved as the
geometry of the underlying surface comes into play. In addition to establishing
bounded Lebesgue constants in this setting, a "zeros lemma" for compact
Riemannian manifolds is established.Comment: 33 pages, 2 figures, new title, accepted for publication in SIAM J.
on Math. Ana
Fractional Operators, Dirichlet Averages, and Splines
Fractional differential and integral operators, Dirichlet averages, and
splines of complex order are three seemingly distinct mathematical subject
areas addressing different questions and employing different methodologies. It
is the purpose of this paper to show that there are deep and interesting
relationships between these three areas. First a brief introduction to
fractional differential and integral operators defined on Lizorkin spaces is
presented and some of their main properties exhibited. This particular approach
has the advantage that several definitions of fractional derivatives and
integrals coincide. We then introduce Dirichlet averages and extend their
definition to an infinite-dimensional setting that is needed to exhibit the
relationships to splines of complex order. Finally, we focus on splines of
complex order and, in particular, on cardinal B-splines of complex order. The
fundamental connections to fractional derivatives and integrals as well as
Dirichlet averages are presented
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