# napari-n2v
[](https://github.com/juglab/napari-n2v/raw/main/LICENSE)
[](https://pypi.org/project/napari-n2v)
[](https://python.org)
[](https://github.com/juglab/napari-n2v/actions)
[](https://codecov.io/gh/juglab/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
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/
Raw data
{
"_id": null,
"home_page": "https://github.com/juglab/napari-n2v",
"name": "napari-n2v",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "",
"keywords": "",
"author": "Tom Burke, Joran Deschamps",
"author_email": "joran.deschamps@fht.org",
"download_url": "https://files.pythonhosted.org/packages/f3/41/a9d6f8cb36f42a91a234b64b33ae6833556e1b9cdc75488507b6d42657f6/napari-n2v-0.1.1.tar.gz",
"platform": null,
"description": "# napari-n2v\n\n[](https://github.com/juglab/napari-n2v/raw/main/LICENSE)\n[](https://pypi.org/project/napari-n2v)\n[](https://python.org)\n[](https://github.com/juglab/napari-n2v/actions)\n[](https://codecov.io/gh/juglab/napari-n2v)\n[](https://napari-hub.org/plugins/napari-n2v)\n\nA self-supervised denoising algorithm now usable by all in napari.\n\n<img src=\"https://raw.githubusercontent.com/juglab/napari-n2v/master/docs/images/noisy_denoised.png\" width=\"800\" />\n----------------------------------\n\n## Installation\n\nCheck out the [documentation](https://juglab.github.io/napari-n2v/installation.html) for more detailed installation \ninstructions. \n\nYou can then start the napari plugin by clicking on \"Plugins > napari_n2v > Training\",\nor run the plugin directly from a [script](scripts/start_plugin.py).\n\n\n\n## Quick demo\n\nYou 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).\n\n\n<img src=\"https://raw.githubusercontent.com/juglab/napari-n2v/master/docs/images/demo.gif\" width=\"800\" />\n\n\n## Documentation\n\nDocumentation is available on the [project website](https://juglab.github.io/napari-n2v/).\n\n\n## Contributing and feedback\n\nContributions are very welcome. Tests can be run with [tox], please ensure\nthe coverage at least stays the same before you submit a pull request. You can also \nhelp us improve by [filing an issue] along with a detailed description or contact us\nthrough the [image.sc](https://forum.image.sc/) forum (tag @jdeschamps).\n\n\n## Citations\n\n### N2V\n\nAlexander Krull, Tim-Oliver Buchholz, and Florian Jug. \"[Noise2void-learning denoising from single noisy images.](https://ieeexplore.ieee.org/document/8954066)\" \n*Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*. 2019.\n\n### structN2V\n\nColeman Broaddus, et al. \"[Removing structured noise with self-supervised blind-spot networks.](https://ieeexplore.ieee.org/document/9098336)\" *2020 IEEE 17th \nInternational Symposium on Biomedical Imaging (ISBI)*. IEEE, 2020.\n\n### N2V2\n\nEva 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). \n\n## Acknowledgements\n\nThis plugin was developed thanks to the support of the Silicon Valley Community Foundation (SCVF) and the \nChan-Zuckerberg Initiative (CZI) with the napari Plugin Accelerator grant _2021-240383_.\n\n\nDistributed under the terms of the [BSD-3] license,\n\"napari-n2v\" is a free and open source software.\n\n[napari]: https://github.com/napari/napari\n[Cookiecutter]: https://github.com/audreyr/cookiecutter\n[@napari]: https://github.com/napari\n[MIT]: http://opensource.org/licenses/MIT\n[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\n[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt\n[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt\n[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0\n[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt\n[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin\n\n[filing an issue]: https://github.com/juglab/napari-n2v/issues\n\n[napari]: https://github.com/napari/napari\n[tox]: https://tox.readthedocs.io/en/latest/\n[pip]: https://pypi.org/project/pip/\n[PyPI]: https://pypi.org/\n",
"bugtrack_url": null,
"license": "BSD-3-Clause",
"summary": "A self-supervised denoising algorithm now usable by all in napari.",
"version": "0.1.1",
"project_urls": {
"Bug Tracker": "https://github.com/juglab/napari-n2v/issues",
"Documentation": "https://juglab.github.io/napari-n2v/",
"Homepage": "https://github.com/juglab/napari-n2v",
"Source Code": "https://github.com/juglab/napari-n2v",
"User Support": "https://github.com/juglab/napari-n2v/issues"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "423180f80a0486a9c48ebf88a34e5d507b9e548def38937d3d1204758504e614",
"md5": "dbe8e656cb1146d3075c369f9c360fcd",
"sha256": "8a413fdd5f9156ca7e58b2db297b2bc2bda0dedbcea18a856815e6e41f9b9a08"
},
"downloads": -1,
"filename": "napari_n2v-0.1.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "dbe8e656cb1146d3075c369f9c360fcd",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 69021,
"upload_time": "2023-08-29T13:30:35",
"upload_time_iso_8601": "2023-08-29T13:30:35.749102Z",
"url": "https://files.pythonhosted.org/packages/42/31/80f80a0486a9c48ebf88a34e5d507b9e548def38937d3d1204758504e614/napari_n2v-0.1.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "f341a9d6f8cb36f42a91a234b64b33ae6833556e1b9cdc75488507b6d42657f6",
"md5": "47ccb572690e8d2026a37ecb42db447d",
"sha256": "1beeafbf66c7f22930534b66ea9783f70b39d1c35a5171fe7910612235d92609"
},
"downloads": -1,
"filename": "napari-n2v-0.1.1.tar.gz",
"has_sig": false,
"md5_digest": "47ccb572690e8d2026a37ecb42db447d",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 60118,
"upload_time": "2023-08-29T13:30:37",
"upload_time_iso_8601": "2023-08-29T13:30:37.290705Z",
"url": "https://files.pythonhosted.org/packages/f3/41/a9d6f8cb36f42a91a234b64b33ae6833556e1b9cdc75488507b6d42657f6/napari-n2v-0.1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-08-29 13:30:37",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "juglab",
"github_project": "napari-n2v",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"tox": true,
"lcname": "napari-n2v"
}
JSON
