{"url":"/dataset/fmd","name":"FMD","full_name":"Fluorescence Microscopy Denoising","description_markdown":"The **Fluorescence Microscopy Denoising** (**FMD**) dataset is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real fluorescence microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. Image averaging is used to effectively obtain ground truth images and 60,000 noisy images with different noise levels.\n\nSource: [https://arxiv.org/abs/1812.10366](https://arxiv.org/abs/1812.10366)\nImage Source: [https://github.com/bmmi/denoising-fluorescence](https://github.com/bmmi/denoising-fluorescence)","description_withheld":null,"homepage":"https://github.com/bmmi/denoising-fluorescence","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/a-poisson-gaussian-denoising-dataset-with","title":"A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images","first_author":"Yide Zhang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Denoising","url":"/task/denoising","datasets_with_task":"/datasets/task/denoising"},{"name":"Image Denoising","url":"/task/image-denoising","datasets_with_task":"/datasets/task/image-denoising"},{"name":"intensity image denoising","url":"/task/intensity-image-denoising","datasets_with_task":"/datasets/task/intensity-image-denoising"},{"name":"Dictionary Learning","url":"/task/dictionary-learning","datasets_with_task":"/datasets/task/dictionary-learning"}],"languages":[],"variants":["FMD"],"data_loaders":[{"repo":"https://github.com/bmmi/denoising-fluorescence","url":"https://github.com/bmmi/denoising-fluorescence","frameworks":["pytorch"]}],"num_papers_in_archive":17,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-denoising-on-fmd","task":"Image Denoising","dataset_variant":"FMD","rows":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"NOise2NOise","paper":"/paper/machine-learning-for-faster-and-smarter","metrics":{"PSNR":"8-10dB"},"code_links":[{"title":"ND-HowardGroup/JPP_review_code_2020","url":"https://github.com/ND-HowardGroup/JPP_review_code_2020"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/intensity-image-denoising-on-fmd","task":"intensity image denoising","dataset_variant":"FMD","rows":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Noise2Noise and DnCNN","paper":"/paper/instant-image-denoising-plugin-for-imagej","metrics":{"PSNR":"7.5dB improvement"},"code_links":[{"title":"varunmannam/Image_denoising","url":"https://github.com/varunmannam/Image_denoising"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/machine-learning-for-faster-and-smarter","title":"Machine learning for faster and smarter fluorescence lifetime imaging microscopy","date":"2020-08-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/instant-image-denoising-plugin-for-imagej","title":"Instant Image Denoising Plugin for ImageJ using Convolutional Neural Networks","date":null,"rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}