# Total variation multiscale estimators for linear inverse problems

## TL;DR

This paper introduces a new estimator for functions of bounded variation in linear inverse problems, achieving near-optimal convergence rates and extending previous results to higher dimensions with a novel analysis of rate regimes.

## Contribution

It proposes a novel variational wavelet-vaguelette estimator for BV functions in inverse problems, providing the first convergence guarantees in dimensions d≥2.

## Key findings

- Estimator is minimax optimal up to logarithmic factors.
- First convergence result for BV functions in inverse problems in dimension d≥2.
- Identification of a slower minimax rate for large q due to low smoothness.

## Abstract

Even though the statistical theory of linear inverse problems is a well-studied topic, certain relevant cases remain open. Among these is the estimation of functions of bounded variation ($BV$), meaning $L^1$ functions on a $d$-dimensional domain whose weak first derivatives are finite Radon measures. The estimation of $BV$ functions is relevant in many applications, since it involves minimal smoothness assumptions and gives simplified, interpretable cartoonized reconstructions. In this paper we propose a novel technique for estimating $BV$ functions in an inverse problem setting, and provide theoretical guaranties by showing that the proposed estimator is minimax optimal up to logarithms with respect to the $L^q$-risk, for any $q\in[1,\infty)$. This is to the best of our knowledge the first convergence result for $BV$ functions in inverse problems in dimension $d\geq 2$, and it extends the results by Donoho (Appl. Comput. Harmon. Anal., 2(2):101--126, 1995) in $d=1$. Furthermore, our analysis unravels a novel regime for large $q$ in which the minimax rate is slower than $n^{-1/(d+2\beta+2)}$, where $\beta$ is the degree of ill-posedness: our analysis shows that this slower rate arises from the low smoothness of $BV$ functions. The proposed estimator combines variational regularization techniques with the wavelet-vaguelette decomposition of operators.