liv-group/reproducible-video-denoising-state-of-the-art
Collection of popular and reproducible video denoising works.
This project compiles a selection of top-performing methods for removing unwanted visual noise from videos. It takes noisy video footage as input and produces cleaner, enhanced video as output, ready for analysis or presentation. Video editors, forensic analysts, or researchers working with raw video data would find this collection useful for improving visual quality.
128 stars. No commits in the last 6 months.
Use this if you need to remove static, grain, or other visual interference from video recordings to improve their clarity and usability.
Not ideal if you are looking to enhance video resolution, stabilize shaky footage, or remove objects from a video.
Stars
128
Forks
14
Language
—
License
—
Category
Last pushed
Feb 25, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/liv-group/reproducible-video-denoising-state-of-the-art"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
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...
NICALab/SUPPORT
Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.
jaewon-lee-b/lte
Local Texture Estimator for Implicit Representation Function, in CVPR 2022