# Risk Estimators for Choosing Regularization Parameters in Ill-Posed Problems - Properties and Limitations

## TL;DR

This paper analyzes the effectiveness of risk estimators like SURE and GSURE for selecting regularization parameters in ill-posed problems, revealing limitations especially as ill-posedness increases, and shows they can lead to unreliable regularization choices.

## Contribution

The paper provides a theoretical and numerical study of risk estimators' properties in ill-posed problems, highlighting their limitations and potential failures in high ill-posedness scenarios.

## Key findings

- GSURE risk estimator deteriorates asymptotically for ill-posed problems
- Risk estimators often suggest overly small regularization parameters
- Unbiased risk estimation may not be reliable for ill-posed inverse problems

## Abstract

This paper discusses the properties of certain risk estimators recently proposed to choose regularization parameters in ill-posed problems. A simple approach is Stein's unbiased risk estimator (SURE), which estimates the risk in the data space, while a recent modification (GSURE) estimates the risk in the space of the unknown variable. It seems intuitive that the latter is more appropriate for ill-posed problems, since the properties in the data space do not tell much about the quality of the reconstruction. We provide theoretical studies of both estimators for linear Tikhonov regularization in a finite dimensional setting and estimate the quality of the risk estimators, which also leads to asymptotic convergence results as the dimension of the problem tends to infinity. Unlike previous papers, who studied image processing problems with a very low degree of ill-posedness, we are interested in the behavior of the risk estimators for increasing ill-posedness. Interestingly, our theoretical results indicate that the quality of the GSURE risk can deteriorate asymptotically for ill-posed problems, which is confirmed by a detailed numerical study. The latter shows that in many cases the GSURE estimator leads to extremely small regularization parameters, which obviously cannot stabilize the reconstruction. Similar but less severe issues with respect to robustness also appear for the SURE estimator, which in comparison to the rather conservative discrepancy principle leads to the conclusion that regularization parameter choice based on unbiased risk estimation is not a reliable procedure for ill-posed problems. A similar numerical study for sparsity regularization demonstrates that the same issue appears in nonlinear variational regularization approaches.