AIMD. AI for microscopy denoising - dataset 1

Link:
Autor/in:
Verlag/Körperschaft:
Universität Hamburg
Erscheinungsjahr:
2025
Medientyp:
Datensatz
Schlagwort:
  • Microscopy, Denoising, Artificial Intelligence, Deep learning
Beschreibung:
  • The dataset contains the first of two processed open-source datasets used in the Github repository:

    https://github.com/IPMI-ICNS-UKE/AIMD.AI-for-microscopy-denoising

    The AIMD Github repository is demonstrating the use of open-source microscopy data for deep learning based image denoising and transfer learning as showcased in:  Lohr, D., Meyer, L., Woelk, LM., Kovacevic, D., Diercks, BP., Werner, R. (2025). Deep Learning-Based Image Restoration and Super-Resolution for Fluorescence Microscopy: Overview and Resources. In: Diercks, BP. (eds) T Cell Activation. Methods in Molecular Biology, vol 2904. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-4414-0_3

    The folder models contains pre-trained denoising models generated using the code of the AIMD repository.

    The original open-source data is the "Fluorescence Microscopy Denoising (FMD) dataset" - CC BY-SA 4.0 license

     

Beziehungen:
DOI 10.25592/uhhfdm.16867
Lizenzen:
  • https://creativecommons.org/licenses/by-sa/4.0/legalcode
  • info:eu-repo/semantics/openAccess
Quellsystem:
Forschungsdatenrepositorium des UKE

Interne Metadaten
Quelldatensatz
oai:fdr.uni-hamburg.de:16868