Automatic regularization parameter choice for tomography using a double model approach
This addresses the challenge of parameter tuning in tomography for researchers and practitioners, but it is incremental as it builds on existing regularization frameworks.
The authors tackled the problem of automatically selecting the regularization parameter in X-ray tomography, which is crucial for image reconstruction with limited data, and demonstrated its effectiveness on real tomographic data.
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.