A self-supervised denoising algorithm now usable by all in napari.
Project description
napari-n2v
A self-supervised denoising algorithm now usable by all in napari.
Installation
Check out the documentation for installation instructions. (soon on PyPi and napari hub)
Quick demo
You can try the quick demo by loading the "N2V Demo prediction" in plugins, and starting the prediction directly.
Documentation
Documentation is available on the project website.
Contributing and feedback
Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request. You can also help us improve by filing an issue along with a detailed description or contact us through the image.sc forum (tag @jdeschamps).
Cite us
N2V
Krull, Alexander, Tim-Oliver Buchholz, and Florian Jug. "Noise2void-learning denoising from single noisy images." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.
structN2V
Broaddus, Coleman, et al. "Removing structured noise with self-supervised blind-spot networks." 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 2020.
Acknowledgements
This plugin was developed thanks to the support of the Silicon Valley Community Foundation (SCVF) and the Chan-Zuckerberg Initiative (CZI) with the napari Plugin Accelerator grant 2021-240383.
Distributed under the terms of the BSD-3 license, "napari-n2v" is a free and open source software.
Project details
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