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Deep-Unfolded Penalized MLEM for Rapid Poisson Image Reconstruction — arXiv:2610.12198

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.12198
License (as archived): CC0-1.0
Archived: 2026-10-09T07:13:49.641Z
SHA-256 of the archived text: e4e12ff84da9f5c8a2701e83d929cb6fd9da897d31691c7caa97f2e53664964c
Stable URL of this page: https://metraipolis.com/documento/dc_995adfa69671

How to cite

Deep-Unfolded Penalized MLEM for Rapid Poisson Image Reconstruction — arXiv:2610.12198 — arXiv (en), https://arxiv.org/abs/2610.12198 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_995adfa69671 (Metraipolis archive dc_995adfa69671, sha256 e4e12ff84da9f5c8a2701e83d929cb6fd9da897d31691c7caa97f2e53664964c)

BibTeX

@misc{dc995adfa69671,
  title = {Deep-Unfolded Penalized MLEM for Rapid Poisson Image Reconstruction — arXiv:2610.12198},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.12198},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_995adfa69671; sha256 e4e12ff84da9f5c8a2701e83d929cb6fd9da897d31691c7caa97f2e53664964c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.12198. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Maximum likelihood expectation maximization (MLEM) is a common approach for Poisson image reconstruction, but accurate recovery requires many iterations and is sensitive to mismatch in the assumed acquisition model. We propose a deep-unfolded penalized MLEM framework that maps a prescribed small number of iterations into trainable layers while retaining the analytical forward/backward projections and the multiplicative structure of MLEM. The unfolded architecture learns layer-dependent regularization and Poisson-model parameters, together with a data-dependent correction of the sensitivity normalization to mitigate model mismatch. We numerically show that our method improves reconstruction resolution at a fixed iteration budget and attains reconstruction quality comparable to long MLEM runs and direct data-driven models.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv eess.SP, nuovi lavori del giorno).
Categorie arXiv: Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2610.12198 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T07:07:57.940Z dalla pagina https://arxiv.org/list/eess.SP/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).