Adaptive Vector-Valued Splines for the Resolution of Inverse Problems

TL;DR

This paper presents a new framework for reconstructing vector-valued functions from noisy data using adaptive L-splines, total variation regularization, and convex optimization, leading to sparse and efficient solutions.

Contribution

It introduces a general approach for inverse problems with vector-valued functions, characterizing solutions as convex hulls of adaptive L-splines with fewer knots, and analyzes the impact of total variation norms.

Findings

Solutions are convex hulls of adaptive L-splines.

Inner norms produce sparser solutions.

Applicable to a broad class of measurement operators.

Abstract

We introduce a general framework for the reconstruction of vector-valued functions from finite and possibly noisy data, acquired through a known measurement operator. The reconstruction is done by the minimization of a loss functional formed as the sum of a convex data fidelity functional and a total-variation-based regularizer involving a suitable matrix L of differential operators. Here, the total variation is a norm on the space of vector measures. These are split into two categories: inner, and outer norms. The minimization is performed over an infinite-dimensional Banach search space. When the measurement operator is weakstar-continuous over the search space, our main result is that the solution set of the loss functional is the closed convex hull of adaptive L-splines, with fewer knots than the number of measurements. We reveal the effect of the total-variation norms on the structure of the solutions and show that inner norms yield sparser solutions. We also provide an explicit description of the class of admissible measurement operators.