mlsanityData to 7 Oct 2026

EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation

arXiv 2112.07111 ↗First posted 14 Dec 2021cs.CVcited by 4

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Abstract (arXiv)

Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related databases, not to mention the databases with GT images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT images, in total 1680 EM images. In this study, in order to test the effectiveness of EMDS-6. We choose the classic algorithms of image processing methods such as image denoising, image segmentation and target detection. The experimental result shows that EMDS-6 can be used to evaluate the performance of image denoising, image segmentation, image feature extraction, image classification, and object detection methods.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 57 references that are in the map. First posted on arXiv 14 Dec 2021.

Builds on

20 of 57 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 0; 2025: 1.

4 papers.

  1. 2022A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurementscited by 1
  2. 2022An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism imagescited by 1
  3. 2025Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusioncited by 1
  4. 2022A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortificationcited by 0