Ground States and Singular Vectors of Convex Variational Regularization Methods

TL;DR

This paper extends the concept of singular values and vectors to nonlinear variational regularization methods for linear inverse problems, focusing on singular one-homogeneous functionals, and demonstrates their utility in understanding scale and reconstructing solutions.

Contribution

It introduces a novel framework for defining and analyzing singular values and vectors in nonlinear regularization, including the ground state concept and higher singular vectors, with applications to inverse problems.

Findings

Ground states can be characterized as the smallest singular values.

Singular vectors can be reconstructed exactly using Tikhonov and inverse scale space methods.

The approach provides scale-dependent estimates and insights into regularization behavior.

Abstract

Singular value decomposition is the key tool in the analysis and understanding of linear regularization methods. In the last decade nonlinear variational approaches such as $\ell^1$ or total variation regularizations became quite prominent regularization techniques with certain properties being superior to standard methods. In the analysis of those, singular values and vectors did not play any role so far, for the obvious reason that these problems are nonlinear, together with the issue of defining singular values and singular vectors. In this paper however we want to start a study of singular values and vectors for nonlinear variational regularization of linear inverse problems, with particular focus on singular one-homogeneous regularization functionals. A major role is played by the smallest singular value, which we define as the ground state of an appropriate functional combining the (semi-)norm introduced by the forward operator and the regularization functional. The optimality condition for the ground state further yields a natural generalization to higher singular values and vectors involving the subdifferential of the regularization functional. We carry over two main properties from the world of linear regularization. The first one is gaining information about scale, respectively the behavior of regularization techniques at different scales. This also leads to novel estimates at different scales, generalizing the estimates for the coefficients in the linear singular value expansion. The second one is to provide exact solutions for variational regularization methods. We will show that all singular vectors can be reconstructed up to a scalar factor by the standard Tikhonov-type regularization approach even in the presence of (small) noise. Moreover, we will show that they can even be reconstructed without any bias by the recently popularized inverse scale space method.