Video Denoising
30 papers with code • 12 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Video Denoising
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
DAVIS
CRVD
VideoLQ
Latest papers
Patch Craft: Video Denoising by Deep Modeling and Patch Matching
grishavak/PaCNet-denoiser
•
•
ICCV 2021
Our algorithm augments video sequences with patch-craft frames and feeds them to a CNN.
Efficient Multi-Stage Video Denoising with Recurrent Spatio-Temporal Fusion
Baymax-chen/EMVD
•
•
CVPR 2021
Then, a denoising stage removes the noise in the fused frame.
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).
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising
xuxy09/texformer
•
•
We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising.
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.
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.
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.
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.
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation
m-tassano/fastdvdnet
•
•
CVPR 2020
In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture.
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.
