Wavelets: a tutorial in theory and applications
Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang–Fix
IEEE Transactions on Signal Processing
Sampling signals with finite rate of innovation
IEEE Transactions on Signal Processing
Irregular sampling theorems for wavelet subspaces
IEEE Transactions on Information Theory
Perturbation of Regular Sampling in Shift-Invariant Spaces for Frames
IEEE Transactions on Information Theory
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Let 驴 be a function in the Wiener amalgam space $\emph{W}_{\infty}(L_1)$ with a non-vanishing property in a neighborhood of the origin for its Fourier transform $\widehat{\phi}$ , ${\bf \tau}=\{\tau_n\}_{n\in {{\mathbb Z}}}$ be a sampling set on 驴 and $V_\phi^{\bf \tau}$ be a closed subspace of $L_2(\hbox{\ensuremath{\mathbb{R}}})$ containing all linear combinations of 驴-translates of 驴. In this paper we prove that every function $f\in V_\phi^{\bf \tau}$ is uniquely determined by and stably reconstructed from the sample set $L_\phi^{\bf \tau}(f)=\Big\{\int_{\hbox{\ensuremath{\mathbb{R}}}} f(t) \overline{\phi(t-\tau_n)} dt\Big\}_{n\in {{\mathbb Z}}}$ . As our reconstruction formula involves evaluating the inverse of an infinite matrix we consider a partial reconstruction formula suitable for numerical implementation. Under an additional assumption on the decay rate of 驴 we provide an estimate to the corresponding error.