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Documentation#

CAREamics is a PyTorch library aimed at simplifying the use of Noise2Void and its many variants and cousins (CARE, Noise2Noise, N2V2, P(P)N2V, HDN, muSplit etc.).

Why CAREamics?#

Noise2Void is a widely used denoising algorithm, and is readily available from the n2v python package. However, n2v is based on TensorFlow and Keras and we found it increasingly hard to maintain. In addition, more recent methods (PPN2V, DivNoising, HDN) are all implemented in PyTorch, but are lacking the extra features that would make them usable by the community.

The aim of CAREamics is to provide a PyTorch library reuniting all the latest methods in one package, while providing a simple and consistent API. The library relies on PyTorch Lightning as a back-end. In addition, we will provide extensive documentation and tutorials on how to best apply these methods in a scientific context.

Work in progress

These pages are still under construction.

Getting Started#

Installation

Get started with CAREamics installation.

Current State

Check out where we stand and where we want to go.

Guides

In-depth guides on CAREamics usage and features.

Applications

Examples of CAREamics in action on various datasets.

Algorithms

Dive into the various CAREamics algorithms.

Code Reference

Code documentation for all CAREamics libraries.

Feedback#

Image.sc

If you are having trouble using the library or the napari plugin, contact us via the Image.sc forum.

We are always welcoming feedback on what to improve of what features could be useful, therefore do not hesitate to open an issue on the Github repository!