8,795 research outputs found
On stable reconstructions from nonuniform Fourier measurements
We consider the problem of recovering a compactly-supported function from a
finite collection of pointwise samples of its Fourier transform taking
nonuniformly. First, we show that under suitable conditions on the sampling
frequencies - specifically, their density and bandwidth - it is possible to
recover any such function in a stable and accurate manner in any given
finite-dimensional subspace; in particular, one which is well suited for
approximating . In practice, this is carried out using so-called nonuniform
generalized sampling (NUGS). Second, we consider approximation spaces in one
dimension consisting of compactly supported wavelets. We prove that a linear
scaling of the dimension of the space with the sampling bandwidth is both
necessary and sufficient for stable and accurate recovery. Thus wavelets are up
to constant factors optimal spaces for reconstruction
Time-frequency analysis of chaotic systems
We describe a method for analyzing the phase space structures of Hamiltonian
systems. This method is based on a time-frequency decomposition of a trajectory
using wavelets. The ridges of the time-frequency landscape of a trajectory,
also called instantaneous frequencies, enable us to analyze the phase space
structures. In particular, this method detects resonance trappings and
transitions and allows a characterization of the notion of weak and strong
chaos. We illustrate the method with the trajectories of the standard map and
the hydrogen atom in crossed magnetic and elliptically polarized microwave
fields.Comment: 36 pages, 18 figure
Some equations relating multiwavelets and multiscaling functions
The local trace function introduced in \cite{Dut} is used to derive equations
that relate multiwavelets and multiscaling functions in the context of a
generalized multiresolution analysis, without appealing to filters. A
construction of normalized tight frame wavelets is given. Particular instances
of the construction include normalized tight frame and orthonormal wavelet
sets
Pairs of Frequency-based Nonhomogeneous Dual Wavelet Frames in the Distribution Space
In this paper, we study nonhomogeneous wavelet systems which have close
relations to the fast wavelet transform and homogeneous wavelet systems. We
introduce and characterize a pair of frequency-based nonhomogeneous dual
wavelet frames in the distribution space; the proposed notion enables us to
completely separate the perfect reconstruction property of a wavelet system
from its stability property in function spaces. The results in this paper lead
to a natural explanation for the oblique extension principle, which has been
widely used to construct dual wavelet frames from refinable functions, without
any a priori condition on the generating wavelet functions and refinable
functions. A nonhomogeneous wavelet system, which is not necessarily derived
from refinable functions via a multiresolution analysis, not only has a natural
multiresolution-like structure that is closely linked to the fast wavelet
transform, but also plays a basic role in understanding many aspects of wavelet
theory. To illustrate the flexibility and generality of the approach in this
paper, we further extend our results to nonstationary wavelets with real
dilation factors and to nonstationary wavelet filter banks having the perfect
reconstruction property
Extreme Value Analysis of Empirical Frame Coefficients and Implications for Denoising by Soft-Thresholding
Denoising by frame thresholding is one of the most basic and efficient
methods for recovering a discrete signal or image from data that are corrupted
by additive Gaussian white noise. The basic idea is to select a frame of
analyzing elements that separates the data in few large coefficients due to the
signal and many small coefficients mainly due to the noise \epsilon_n. Removing
all data coefficients being in magnitude below a certain threshold yields a
reconstruction of the original signal. In order to properly balance the amount
of noise to be removed and the relevant signal features to be kept, a precise
understanding of the statistical properties of thresholding is important. For
that purpose we derive the asymptotic distribution of max_{\omega \in \Omega_n}
|| for a wide class of redundant frames
(\phi_\omega^n: \omega \in \Omega_n}. Based on our theoretical results we give
a rationale for universal extreme value thresholding techniques yielding
asymptotically sharp confidence regions and smoothness estimates corresponding
to prescribed significance levels. The results cover many frames used in
imaging and signal recovery applications, such as redundant wavelet systems,
curvelet frames, or unions of bases. We show that `generically' a standard
Gumbel law results as it is known from the case of orthonormal wavelet bases.
However, for specific highly redundant frames other limiting laws may occur. We
indeed verify that the translation invariant wavelet transform shows a
different asymptotic behaviour.Comment: [Content: 39 pages, 4 figures] Note that in this version 4 we have
slightely changed the title of the paper and we have rewritten parts of the
introduction. Except for corrected typos the other parts of the paper are the
same as the original versions
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