Nonideal Sampling and Regularization Theory

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) 2-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 ...

• Type: Journal
• Year: 2008
• Where: TSP
• Authors: Sathish Ramani, Dimitri Van De Ville, Thierry Blu, Michael Unser
• Keywords: Continuous-space Solution, Reconstruction Space, Solutions, TSP 2008