napari-n2v

napari-n2v brings Noise2Void to the fantastic world of napari. N2V is a sef-supervised denoising algorithm allowing removing pixel-independent noise. It also includes an extension, structN2V, aimed at removing structured noise.

This set of plugins can train, retrain and predict on images from napari or from the disk. It conveniently allows saving the models for later use and is compatible with Bioimage.io.

Documentation

Report issues and errors

Help us improve the plugin by submitting issues to the Github repository or tagging @jdeschamps on image.sc.

Citation

N2V

Alexander Krull, 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

Coleman Broaddus, et al. “Removing structured noise with self-supervised blind-spot networks.” 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”, (2022).

Support

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.