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  4. Nonideal sampling and regularization theory
 
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Nonideal sampling and regularization theory

Journal
IEEE Transactions on Signal Processing
Journal Volume
56
Journal Issue
3
Date Issued
2008-03-01
Author(s)
Ramani, Sathish
Van De Ville, Dimitri
THIERRY BLU  
Unser, Michael
DOI
10.1109/TSP.2007.908997
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/640589
URL
https://api.elsevier.com/content/abstract/scopus_id/40749094435
Abstract
Shannon's sampling theory and its variants provide effective solutions to the problem of reconstructing a signal from its samples in some "shift-invariant" space, which may or may not be bandlimited. In this paper, we present some further justification for this type of representation, while addressing the issue of the specification of the best reconstruction space. We consider a realistic setting where a multidimensional signal is prefiltered prior to sampling, and the samples are corrupted by additive noise. We adopt a variational approach to the reconstruction problem and minimize a data fidelity term subject to a Tikhonov-like (continuous domain) L2-regularization to obtain the continuous-space solution. We present theoretical justification for the minimization of this cost functional and show that the globally minimal continuous-space solution belongs to a shift-invariant space generated by a function (generalized B-spline) that is generally not bandlimited. When the sampling is ideal, we recover some of the classical smoothing spline estimators. The optimal reconstruction space is characterized by a condition that links the generating function to the regularization operator and implies the existence of a B-spline-like basis. To make the scheme practical, we specify the generating functions corresponding to the most popular families of regularization operators (derivatives, iterated Laplacian), as well as a new, generalized one that leads to a new brand of Matérn splines. We conclude the paper by proposing a stochastic interpretation of the reconstruction algorithm and establishing an equivalence with the minimax and minimum mean square error (MMSE/Wiener) solutions of the generalized sampling problem. © 2008 IEEE.
Subjects
Mateacute;rn class | Minimax and minimum mean square error (MMSE) reconstruction | Nonideal sampling | Regularization | Shift-invariant spaces | Smoothing splines | Tikhonov criterion | Wiener solution
Type
journal article

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