AIMD. AI for microscopy denoising - dataset 2
- Link:
- Autor/in:
- Verlag/Körperschaft:
- Universität Hamburg
- Erscheinungsjahr:
- 2025
- Medientyp:
- Datensatz
- Schlagwort:
-
- Microscopy, Denoising, Artificial Intelligence, Deep learning
- Beschreibung:
-
The dataset contains the second of two processed open-source datasets used in the Github repository:
https://github.com/IPMI-ICNS-UKE/AIMD.AI-for-microscopy-denoisingThe 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 original open-source data is from "Fluorescence Microscopy Datasets for Training Deep Neural Networks" - CC0 license
• http://gigadb.org/dataset/view/id/100888
• 16 bit image data, filetype: tif
- Lizenzen:
-
- https://creativecommons.org/publicdomain/zero/1.0/legalcode
- info:eu-repo/semantics/openAccess
- Quellsystem:
- Forschungsdatenrepositorium des UKE
Interne Metadaten
- Quelldatensatz
- oai:fdr.uni-hamburg.de:16914