Badoual, AnaisFageot, JulienUnser, Michael2018-12-132018-12-132018-12-132018-11-1510.1109/TSP.2018.2873514https://infoscience.epfl.ch/handle/20.500.14299/151855WOS:000447853700006This 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 trade off between fidelity to the data and smoothness conditions via a quadratic regularization associated with 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.Engineering, Electrical & ElectronicEngineeringperiodic signalsvariational methodsrepresenter theoremgaussian processesmmse estimatorssplinesgeneralized tv regularizationcardinal exponential splinesimage-restorationpart istochastic processesself-similarityfinite ratesignalreconstructioninnovationstext::journal::journal article::research article