# Automatic regularization parameter choice for tomography using a double model approach

arXiv: https://arxiv.org/abs/2602.08528 · HTML: https://mlsanity.com/paper/2602.08528/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [Bilevel parameter learning for higher-order total variation regularisation models](https://mlsanity.com/paper/1508.07243/) (2015)
- [Linearly convergent bilevel optimization with single-step inner methods](https://mlsanity.com/paper/2205.04862/) (2022)
- [Constrained Regularization by Denoising with Automatic Parameter Selection](https://mlsanity.com/paper/2311.03819/) (2023)

## Abstract (arXiv)

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.