{"name":"napari-n2v","display_name":"napari n2v","visibility":"public","icon":"","categories":[],"schema_version":"0.2.0","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-n2v.make_n2v_trainwidget","title":"Make N2V widget","python_name":"napari_n2v._train_widget:TrainingWidgetWrapper","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-n2v.make_n2v_predictwidget","title":"Make N2V predict widget","python_name":"napari_n2v._predict_widget:PredictWidgetWrapper","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-n2v.make_n2v_demo_prediction","title":"Make N2V demo prediction","python_name":"napari_n2v._predict_widget:DemoPrediction","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-n2v.data_2D","title":"N2V 2D data","python_name":"napari_n2v._sample_data:n2v_2D_data","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-n2v.data_3D","title":"N2V 3D data","python_name":"napari_n2v._sample_data:n2v_3D_data","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-n2v.data_RGB","title":"N2V RGB data","python_name":"napari_n2v._sample_data:n2v_rgb_data","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-n2v.data_SEM","title":"N2V SEM data","python_name":"napari_n2v._sample_data:n2v_sem_data","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"napari-n2v.make_n2v_trainwidget","display_name":"N2V Train","autogenerate":false},{"command":"napari-n2v.make_n2v_predictwidget","display_name":"N2V Predict","autogenerate":false},{"command":"napari-n2v.make_n2v_demo_prediction","display_name":"N2V Demo prediction","autogenerate":false}],"sample_data":[{"command":"napari-n2v.data_2D","key":"napari_n2v_2D_data","display_name":"Download data (2D)"},{"command":"napari-n2v.data_3D","key":"napari_n2v_3D_data","display_name":"Download data (3D)"},{"command":"napari-n2v.data_RGB","key":"napari_n2v_RGB_data","display_name":"Download data (RGB)"},{"command":"napari-n2v.data_SEM","key":"napari_n2v_SEM_data","display_name":"Download data (SEM)"}],"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.1","name":"napari-n2v","version":"0.1.1","dynamic":null,"platform":null,"supported_platform":null,"summary":"A self-supervised denoising algorithm now usable by all in napari.","description":"# napari-n2v\n\n[![License](https://img.shields.io/pypi/l/napari-n2v.svg?color=green)](https://github.com/juglab/napari-n2v/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/napari-n2v.svg?color=green)](https://pypi.org/project/napari-n2v)\n[![Python Version](https://img.shields.io/pypi/pyversions/napari-n2v.svg?color=green)](https://python.org)\n[![tests](https://github.com/juglab/napari-n2v/workflows/build/badge.svg)](https://github.com/juglab/napari-n2v/actions)\n[![codecov](https://codecov.io/gh/juglab/napari-n2v/branch/main/graph/badge.svg)](https://codecov.io/gh/juglab/napari-n2v)\n[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-n2v)](https://napari-hub.org/plugins/napari-n2v)\n\nA self-supervised denoising algorithm now usable by all in napari.\n\n\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\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","description_content_type":"text/markdown","keywords":null,"home_page":"https://github.com/juglab/napari-n2v","download_url":null,"author":"Tom Burke, Joran Deschamps","author_email":"joran.deschamps@fht.org","maintainer":null,"maintainer_email":null,"license":"BSD-3-Clause","classifier":["Framework :: napari","Development Status :: 4 - Beta","Intended Audience :: Science/Research","Topic :: Scientific/Engineering :: Image Processing","Topic :: Scientific/Engineering :: Information Analysis","Programming Language :: Python","Programming Language :: Python :: 3.8","Programming Language :: Python :: 3.9","Programming Language :: Python :: 3.10","Operating System :: OS Independent","License :: OSI Approved :: BSD License"],"requires_dist":["scikit-image","bioimageio.core","n2v >=0.3.2","napari-time-slicer >=0.4.9","napari","qtpy","pyqtgraph","tensorflow >=2.10.0 ; platform_system != \"Darwin\" or platform_machine != \"arm64\"","tensorflow-macos ; platform_system == \"Darwin\" and platform_machine == \"arm64\"","tensorflow-metal ; platform_system == \"Darwin\" and platform_machine == \"arm64\"","numpy <1.24.0 ; python_version < \"3.9\"","numpy ; python_version >= \"3.9\"","pytest ; extra == 'testing'","pytest-cov ; extra == 'testing'","pytest-qt ; extra == 'testing'","pyqt5 ; extra == 'testing'"],"requires_python":">=3.8","requires_external":null,"project_url":["Bug Tracker, https://github.com/juglab/napari-n2v/issues","Documentation, https://juglab.github.io/napari-n2v/","Source Code, https://github.com/juglab/napari-n2v","User Support, https://github.com/juglab/napari-n2v/issues"],"provides_extra":["testing"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}