Comparison of deep learning approaches for extreme low-SNR image restoration.

Buhn NE, Adunur SR, Hamilton J, Levis S, Hagen GM, Ventura JD

DOI: 10.1093/gigascience/giag071
Published: 2026 Jan 21
Container: GigaScience
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1093/gigascience/giag071,
  title = {Comparison of deep learning approaches for extreme low-SNR image restoration.},
  author = {Buhn NE and Adunur SR and Hamilton J and Levis S and Hagen GM and Ventura JD},
  year = {2026},
  journal = {GigaScience},
  doi = {10.1093/gigascience/giag071},
  url = {https://doi.org/10.1093/gigascience/giag071}
}

RIS

TY  - JOUR
TI  - Comparison of deep learning approaches for extreme low-SNR image restoration.
AU  - Buhn NE
AU  - Adunur SR
AU  - Hamilton J
AU  - Levis S
AU  - Hagen GM
AU  - Ventura JD
PY  - 2026
JO  - GigaScience
DO  - 10.1093/gigascience/giag071
UR  - https://doi.org/10.1093/gigascience/giag071
ER  -

APA

NE, B., SR, A., J, H., S, L., GM, H., & JD, V. (2026). Comparison of deep learning approaches for extreme low-SNR image restoration.. GigaScience. https://doi.org/10.1093/gigascience/giag071

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pubmed · retrieved 2026-09-25T07:37:32.842Z