Generalized Minkowski sets for the regularization of inverse problems

TL;DR

This paper introduces a generalized Minkowski set framework for inverse problem regularization, enabling the integration of multiple prior constraints on model components and their sum, improving imaging results.

Contribution

It extends Minkowski set theory to incorporate multiple constraints on components and their sum, facilitating more flexible and informed regularization in inverse problems.

Findings

Enhanced seismic waveform inversion results.

Improved video background-anomaly separation.

Effective incorporation of multiple priors in inverse problems.

Abstract

Many works on inverse problems in the imaging sciences consider regularization via one or more penalty functions or constraint sets. When the models/images are not easily described using one or a few penalty functions/constraints, additive model descriptions for regularization lead to better imaging results. These include cartoon-texture decomposition, morphological component analysis, and robust principal component analysis; methods that typically rely on penalty functions. We propose a regularization framework, based on the Minkowski set, that merges the strengths of additive models and constrained formulations. We generalize the Minkowski set, such that the model parameters are the sum of two components, each of which is constrained to an intersection of sets. Furthermore, the sum of the components is also an element of another intersection of sets. These generalizations allow us to include multiple pieces of prior knowledge on each of the components, as well as on the sum of components, which is necessary to ensure physical feasibility of partial-differential-equation based parameters estimation problems. We derive the projection operation onto the generalized Minkowski sets and construct an algorithm based on the alternating direction method of multipliers. We illustrate how we benefit from using more prior knowledge in the form of the generalized Minkowski set using seismic waveform inversion and video background-anomaly separation.