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DeepVIDv2

imported

software/deepvidv2

DeepVID v2: Self-Supervised Denoising with Decoupled Spatiotemporal Enhancement for Low-Photon Voltage Imaging

Machine-generated from the listed sources and not yet reviewed by a human.

DeepVIDv2 project image
GitHub preview card for bu-cisl/DeepVIDv2. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
GPL-3.0(osi)
Status
dormant
Maturity
deployed
Organization
bu-cisl
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
deep-learning · denoising · microscopy · self-supervised-learning · voltage-imaging
Regulatory
unknown
built by · 1

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.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • SUPPORTdenoising · microscopy · self-supervised-learning

    Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.

  • deepinvdenoising · microscopy

    DeepInverse: a PyTorch library for solving imaging inverse problems using deep learning

  • cell_observatory_platformmicroscopy · self-supervised-learning

    Training backend for Cell Observatory models

  • event_super-resolutionmicroscopy · self-supervised-learning

    Repo for Neuromorphic Imaging with Super-Resolution, IEEE TCSVT, 2025.

  • ZAugNetmicroscopy · self-supervised-learning

    ZAugNet

  • BiaPydenoising

    Open source Python library for building bioimage analysis pipelines

sources
  1. api.github.com/repos/bu-cisl/DeepVIDv2
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2024-05-23, 11 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 →

machine-readable

/v1/entries/22.json→ .entries["deepvidv2"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.