AIMD. AI for microscopy denoising - dataset 1
- Link:
- Autor/in:
- Verlag/Körperschaft:
- Universität Hamburg
- Erscheinungsjahr:
- 2025
- Medientyp:
- Datensatz
- Schlagwort:
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- Microscopy, Denoising, Artificial Intelligence, Deep learning
- Beschreibung:
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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
- https://curate.nd.edu/articles/dataset/Fluorescence_Microscopy_Denoising_FMD_dataset/24744648
- 8 bit image data, filetype: png
- Lizenzen:
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- 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