Automatic regularization parameter choice for tomography using a double model approach

TL;DR

This paper introduces a novel automatic regularization parameter selection method for X-ray tomography, using a double model approach and feedback control to improve reconstruction quality with limited data.

Contribution

A new method employing two discretizations and feedback control for automatic regularization parameter choice in tomography is proposed.

Findings

Effective parameter selection on real data

Improved reconstruction quality with limited data

Automatic and adaptive process

Abstract

Image reconstruction in X-ray tomography is an ill-posed inverse problem, particularly with limited available data. Regularization is thus essential, but its effectiveness hinges on the choice of a regularization parameter that balances data fidelity against a priori information. We present a novel method for automatic parameter selection based on the use of two distinct computational discretizations of the same problem. A feedback control algorithm dynamically adjusts the regularization strength, driving an iterative reconstruction toward the smallest parameter that yields sufficient similarity between reconstructions on the two grids. The effectiveness of the proposed approach is demonstrated using real tomographic data.