AIMD. AI for microscopy denoising - dataset 1

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

本数据集为该GitHub仓库中所使用的两份经过预处理的开源数据集之一，对应仓库地址为：https://github.com/IPMI-ICNS-UKE/AIMD.AI-for-microscopy-denoising

该AIMD GitHub仓库旨在演示如何将开源显微镜数据应用于基于深度学习的图像去噪与迁移学习，相关研究成果详见：Lohr, D., Meyer, L., Woelk, LM., Kovacevic, D., Diercks, BP., Werner, R. (2025). 基于深度学习的荧光显微镜图像复原与超分辨率：综述与资源. 收录于：Diercks, BP. (编) 《T细胞活化》. 《分子生物学方法》，第2904卷. Humana出版社，纽约州纽约市. https://doi.org/10.1007/978-1-0716-4414-0_3

models文件夹内存储了使用AIMD仓库代码训练得到的预训练去噪模型。

本数据集的原始开源数据为荧光显微镜去噪（Fluorescence Microscopy Denoising, FMD）数据集，采用CC BY-SA 4.0许可协议，原始数据访问地址为：https://curate.nd.edu/articles/dataset/Fluorescence_Microscopy_Denoising_FMD_dataset/24744648

该数据为8位图像，文件格式为PNG。