cxm12/UNiFMIR

Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration

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Emerging

This project helps scientists and researchers improve the quality of their fluorescence microscopy images. It takes blurry, noisy, or low-resolution microscopy images and transforms them into clearer, sharper, and higher-resolution versions. This tool is designed for biologists, microscopists, and lab technicians who work with optical microscopy.

No commits in the last 6 months.

Use this if you need to restore or enhance fluorescence microscopy images for better visualization, analysis, or publication, especially across different types of image restoration tasks like denoising or super-resolution.

Not ideal if you are working with other types of images outside of fluorescence microscopy, or if you need to train a model from scratch without existing foundational models.

fluorescence-microscopy bioimaging image-restoration scientific-research lab-workflow
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 9 / 25

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Stars

65

Forks

5

Language

Python

License

GPL-3.0

Last pushed

Apr 24, 2024

Commits (30d)

0

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