napari-n2v 0.1.1
A self-supervised denoising algorithm now usable by all in napari.
License: BSD-3-Clause
Requires Python: >=3.8

# napari-n2v

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A self-supervised denoising algorithm now usable by all in napari.

<img src="https://raw.githubusercontent.com/juglab/napari-n2v/master/docs/images/noisy_denoised.png" width="800" />
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## Installation

Check out the [documentation](https://juglab.github.io/napari-n2v/installation.html) for more detailed installation 
instructions. 

You can then start the napari plugin by clicking on "Plugins > napari_n2v > Training",
or run the plugin directly from a [script](scripts/start_plugin.py).



## Quick demo

You can try out a demo by loading the `N2V Demo prediction` plugin and directly clicking on `Predict`. This model was trained using the [N2V2 example](https://juglab.github.io/napari-n2v/examples.html).


<img src="https://raw.githubusercontent.com/juglab/napari-n2v/master/docs/images/demo.gif" width="800" />


## Documentation

Documentation is available on the [project website](https://juglab.github.io/napari-n2v/).


## 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](https://forum.image.sc/) forum (tag @jdeschamps).


## Citations

### N2V

Alexander Krull, Tim-Oliver Buchholz, and Florian Jug. "[Noise2void-learning denoising from single noisy images.](https://ieeexplore.ieee.org/document/8954066)" 
*Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*. 2019.

### structN2V

Coleman Broaddus, et al. "[Removing structured noise with self-supervised blind-spot networks.](https://ieeexplore.ieee.org/document/9098336)" *2020 IEEE 17th 
International Symposium on Biomedical Imaging (ISBI)*. IEEE, 2020.

### N2V2

Eva Hoeck, Tim-Oliver Buchholz, et al. "[N2V2 - Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture](https://arxiv.org/abs/2211.08512)", arXiv (2022). 

## 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.

[napari]: https://github.com/napari/napari
[Cookiecutter]: https://github.com/audreyr/cookiecutter
[@napari]: https://github.com/napari
[MIT]: http://opensource.org/licenses/MIT
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt
[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt
[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0
[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt
[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin

[filing an issue]: https://github.com/juglab/napari-n2v/issues

[napari]: https://github.com/napari/napari
[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/