SUPPORT
importedsoftware/support
Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- GPL-3.0(osi)
- Status
- maintained
- Maturity
- deployed
- Organization
- NICALab
- Country
- unknown
- Repository
- github.com/NICALab/SUPPORT
- Documentation
- unknown
- Tags
- calcium-imaging · deep-learning · denoising · microscopy · neural-network · self-supervised-learning · structural-imaging · time-lapse-imaging
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- DeepVIDv2denoising · microscopy · self-supervised-learning
DeepVID v2: Self-Supervised Denoising with Decoupled Spatiotemporal Enhancement for Low-Photon Voltage Imaging
- miocalcium-imaging · microscopy
miniscope I/O sdk
- deepinvdenoising · microscopy
DeepInverse: a PyTorch library for solving imaging inverse problems using deep learning
- AutoStereotacalcium-imaging
An open-source automated surgical instrument for microendoscope implantation
- BiaPydenoising
Open source Python library for building bioimage analysis pipelines
- dbMAPdenoising
Deprecated in favour of TopoMetry: https://github.com/davisidarta/topometry
- api.github.com/repos/NICALab/SUPPORTretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2025-08-11, 108 stars, license reported as GPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/40.json→ .entries["support"]
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