Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part I: Methodology and Experiments

TL;DR

This paper introduces an empirical Bayesian method for automatically estimating regularisation parameters in high-dimensional inverse imaging problems, improving robustness and ease of use across various applications.

Contribution

It presents a novel maximum marginal likelihood approach that calibrates multiple regularisation parameters directly from data, compatible with proximal algorithms.

Findings

Effective in image denoising and deconvolution

Outperforms some existing parameter setting methods

Applicable to diverse regularisation priors

Abstract

Many imaging problems require solving an inverse problem that is ill-conditioned or ill-posed. Imaging methods typically address this difficulty by regularising the estimation problem to make it well-posed. This often requires setting the value of the so-called regularisation parameters that control the amount of regularisation enforced. These parameters are notoriously difficult to set a priori, and can have a dramatic impact on the recovered estimates. In this work, we propose a general empirical Bayesian method for setting regularisation parameters in imaging problems that are convex w.r.t. the unknown image. Our method calibrates regularisation parameters directly from the observed data by maximum marginal likelihood estimation, and can simultaneously estimate multiple regularisation parameters. Furthermore, the proposed algorithm uses the same basic operators as proximal optimisation algorithms, namely gradient and proximal operators, and it is therefore straightforward to apply to problems that are currently solved by using proximal optimisation techniques. Our methodology is demonstrated with a range of experiments and comparisons with alternative approaches from the literature. The considered experiments include image denoising, non-blind image deconvolution, and hyperspectral unmixing, using synthesis and analysis priors involving the L1, total-variation, total-variation and L1, and total-generalised-variation pseudo-norms. A detailed theoretical analysis of the proposed method is presented in the companion paper arXiv:2008.05793.