# Revisiting PsiDONet: microlocally inspired filters for incomplete-data tomographic reconstructions

arXiv: https://arxiv.org/abs/2501.18219 · HTML: https://mlsanity.com/paper/2501.18219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2025.

## Abstract (arXiv)

In this paper, we revisit a supervised learning approach based on unrolling, known as PsiDONet, 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 PsiDONet 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.