CAREamics (Jug Lab, EMBL / Human Technopole)
github.com/careamics/careamicsDeep-learning library for AI microscopy image restoration implementing CARE (content-aware restoration, Nature Methods 2018), Noise2Void/Noise2Fast self-supervised denoising, and related pixel-restoration algorithms as a unified, pip-installable training/inference API on a PyTorch Lightning backend, with Fiji plugin and napari integration for denoising, deconvolution, super-resolution, and virtual-staining workflows across light and electron microscopy (successor to CSBDeep, 157+ stars, BSD-3-Clause, actively maintained, 2023-2026)
Sourced from
- Awesome AI for Science — github.com/careamics/careamics
- GitHub — github.com/careamics/careamics
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