In this paper, we revisit a supervised learning approach based on unrolling, known as ΨDONet, by providing a deeper microlocal interpretation for its theoretical analysis, and extending its study to the case of sparse-angle tomography. Furthermore, we refine the implementation of the original ΨDONet considering special filters whose structure is specifically inspired by the streak artifact singularities characterizing tomographic reconstructions from incomplete data. This allows to considerably lower the number of (learnable) parameters while preserving (or even slightly improving) the same quality for the reconstructions from limited-angle data and providing a proof-of-concept for the case of sparse-angle tomographic data.
@article{arxiv.2501.18219,
title = {Revisiting $\Psi$DONet: microlocally inspired filters for incomplete-data tomographic reconstructions},
author = {Tatiana A. Bubba and Luca Ratti and Andrea Sebastiani},
journal= {arXiv preprint arXiv:2501.18219},
year = {2025}
}