NICALab/SUPPORT
Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.
This tool helps researchers and scientists clean up noisy voltage imaging data, as well as other functional, volumetric, and timelapse microscopy data. You feed it your raw, noisy image files, and it produces a clear, denoised version, allowing for more precise analysis. It's designed for biologists, neuroscientists, or anyone working with microscopy images who needs to improve data quality without needing extra reference images.
100 stars. No commits in the last 6 months.
Use this if you need to precisely remove noise from voltage imaging, time-lapse fluorescence microscopy, or static volumetric imaging data to reveal underlying dynamics.
Not ideal if you do not have access to a GPU, especially for training the denoising network on your specific datasets.
Stars
100
Forks
21
Language
Python
License
GPL-3.0
Category
Last pushed
Aug 11, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/NICALab/SUPPORT"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
CAREamics/careamics
A deep-learning library for denoising images using Noise2Void and friends (CARE, PN2V, HDN...
yu4u/noise2noise
An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration...
rgeirhos/texture-vs-shape
Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased...
jaewon-lee-b/lte
Local Texture Estimator for Implicit Representation Function, in CVPR 2022
cabooster/DeepCAD-RT
DeepCAD-RT: Real-time denoising of fluorescence time-lapse imaging using deep self-supervised learning