Metadata-Version: 2.1
Name: napari-n2v
Version: 0.0.3
Summary: A self-supervised denoising algorithm now usable by all in napari.
Home-page: https://github.com/juglab/napari-n2v
Author: Tom Burke, Joran Deschamps
Author-email: joran.deschamps@fht.org
License: BSD-3-Clause
Project-URL: Bug Tracker, https://github.com/juglab/napari-n2v/issues
Project-URL: Documentation, https://juglab.github.io/napari-n2v/
Project-URL: Source Code, https://github.com/juglab/napari-n2v
Project-URL: User Support, https://github.com/juglab/napari-n2v/issues
Classifier: Framework :: napari
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: BSD License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: magicgui
Requires-Dist: qtpy
Requires-Dist: n2v (>=0.3.2)
Requires-Dist: bioimageio.core
Requires-Dist: pyqtgraph
Requires-Dist: scikit-image
Requires-Dist: napari-time-slicer (>=0.4.9)
Requires-Dist: napari (<=0.4.15)
Requires-Dist: vispy (<=0.9.6)
Requires-Dist: imageio (!=2.11.0,!=2.22.1,>=2.5.0)
Provides-Extra: testing
Requires-Dist: pytest ; extra == 'testing'
Requires-Dist: pytest-cov ; extra == 'testing'
Requires-Dist: pytest-qt ; extra == 'testing'
Requires-Dist: napari ; extra == 'testing'
Requires-Dist: pyqt5 ; extra == 'testing'
Requires-Dist: tensorflow ; (platform_system != "Darwin" or platform_machine != "arm64") and extra == 'testing'
Requires-Dist: tensorflow-macos ; (platform_system == "Darwin" and platform_machine == "arm64") and extra == 'testing'
Requires-Dist: tensorflow-metal ; (platform_system == "Darwin" and platform_machine == "arm64") and extra == 'testing'

# napari-n2v

[![License](https://img.shields.io/pypi/l/napari-n2v.svg?color=green)](https://github.com/juglab/napari-n2v/raw/main/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/napari-n2v.svg?color=green)](https://pypi.org/project/napari-n2v)
[![Python Version](https://img.shields.io/pypi/pyversions/napari-n2v.svg?color=green)](https://python.org)
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[![codecov](https://codecov.io/gh/juglab/napari-n2v/branch/main/graph/badge.svg)](https://codecov.io/gh/juglab/napari-n2v)
[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-n2v)](https://napari-hub.org/plugins/napari-n2v)

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" />
----------------------------------

## Installation

You can install `napari-n2v` via [pip]:
```bash
    pip install napari-n2v
```
Or through the [napari-hub](https://napari.org/stable/plugins/find_and_install_plugin.html).


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


## 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://openreview.net/forum?id=IZfQYb4lHVq)", (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/
