TV-based Reconstruction of Periodic Functions

TL;DR

This paper presents a new convex optimization framework for reconstructing periodic multivariate functions from limited, noisy measurements using sparsity-promoting regularization with periodic L-splines.

Contribution

It introduces a periodic representer theorem showing solutions are periodic L-splines with fewer knots than measurements, broadening reconstruction methods for various measurement types.

Findings

Solutions are periodic L-splines with fewer knots than measurements.

Framework applies to various measurement functionals and regularization operators.

Effective for both univariate and multivariate periodic functions.

Abstract

We introduce a general framework for the reconstruction of periodic multivariate functions from finitely many and possibly noisy linear measurements. The reconstruction task is formulated as a penalized convex optimization problem, taking the form of a sum between a convex data fidelity functional and a sparsity-promoting total variation based penalty involving a suitable spline-admissible regularizing operator L. In this context, we establish a periodic representer theorem, showing that the extreme-point solutions are periodic L-splines with less knots than the number of measurements. The main results are specified for the broadest classes of measurement functionals, spline-admissible operators, and convex data fidelity functionals. We exemplify our results for various regularization operators and measurement types (e.g., spatial sampling, Fourier sampling, or square-integrable functions). We also consider the reconstruction of both univariate and multivariate periodic functions.