Video Denoising
30 papers with code • 12 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Video Denoising
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
DAVIS
CRVD
VideoLQ
Most implemented papers
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising
Transform learning methods involve cheap computations and have been demonstrated to perform well in applications such as image denoising and medical image reconstruction.
Model-blind Video Denoising Via Frame-to-frame Training
Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available.
ViDeNN: Deep Blind Video Denoising
We propose ViDeNN: a CNN for Video Denoising without prior knowledge on the noise distribution (blind denoising).
Deep Blind Video Decaptioning by Temporal Aggregation and Recurrence
shwoo93/video_decaptioning
•
•
CVPR 2019
Blind video decaptioning is a problem of automatically removing text overlays and inpainting the occluded parts in videos without any input masks.
DVDnet: A Fast Network for Deep Video Denoising
m-tassano/dvdnet
•
•
Previous neural network based approaches to video denoising have been unsuccessful as their performance cannot compete with the performance of patch-based methods.
First image then video: A two-stage network for spatiotemporal video denoising
This two-stage network, when trained in an end-to-end fashion, yields the state-of-the-art performances on the video denoising benchmark Vimeo90K dataset in terms of both denoising quality and computation.
Implementation of the VBM3D Video Denoising Method and Some Variants
VBM3D is an extension to video of the well known image denoising algorithm BM3D, which takes advantage of the sparse representation of stacks of similar patches in a transform domain.
Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes
cao-cong/RViDeNet
•
•
CVPR 2020
Clean video frames for dynamic scenes cannot be captured with a long-exposure shutter or averaging multi-shots as was done for static images.
Unsupervised Deep Video Denoising
sreyas-mohan/udvd
•
•
ICCV 2021
This is advantageous because motion compensation is computationally expensive, and can be unreliable when the input data are noisy.
Multi-Stage Raw Video Denoising with Adversarial Loss and Gradient Mask
We propose to do this by first explicitly aligning the neighboring frames to the current frame using a convolutional neural network (CNN).
