Video Super-Resolution
132 papers with code • 15 benchmarks • 13 datasets
Video Super-Resolution is a computer vision task that aims to increase the resolution of a video sequence, typically from lower to higher resolutions. The goal is to generate high-resolution video frames from low-resolution input, improving the overall quality of the video.
( Image credit: Detail-revealing Deep Video Super-Resolution )
Benchmarks
These leaderboards are used to track progress in Video Super-Resolution
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Video Super-Resolution models and implementationsDatasets
Vimeo90K
MSU SR-QA Dataset
MSU Video Super Resolution Benchmark: Detail Restoration
MSU Video Upscalers: Quality Enhancement
MSU Super-Resolution for Video Compression
Inter4K
TbD-3D
TbD
Falling Objects
Latest papers with no code
FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
Face video restoration (FVR) is a challenging but important problem where one seeks to recover a perceptually realistic face videos from a low-quality input.
RBPGAN: Recurrent Back-Projection GAN for Video Super Resolution
Recently, video super resolution (VSR) has become a very impactful task in the area of Computer Vision due to its various applications.
An End-Cloud Computing Enabled Surveillance Video Transmission System
The enormous data volume of video poses a significant burden on the network.
HSTR-Net: Reference Based Video Super-resolution for Aerial Surveillance with Dual Cameras
Aerial surveillance requires high spatio-temporal resolution (HSTR) video for more accurate detection and tracking of objects.
Video Super-Resolution Using a Grouped Residual in Residual Network
Super-resolution (SR) is the technique of increasing the nominal resolution of image / video content accompanied with quality improvement.
SimDA: Simple Diffusion Adapter for Efficient Video Generation
In this work, we propose a Simple Diffusion Adapter (SimDA) that fine-tunes only 24M out of 1. 1B parameters of a strong T2I model, adapting it to video generation in a parameter-efficient way.
RefVSR++: Exploiting Reference Inputs for Reference-based Video Super-resolution
Then, we propose an improved method, RefVSR++, which can aggregate two features in parallel in the temporal direction, one for aggregating the fused LR and Ref inputs and the other for Ref inputs over time.
NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution
On the contrary, simple combinations of classical degradation are used for real-world noise modeling, which led to the VSR model often being violated by out-of-distribution noise.
Can SAM Boost Video Super-Resolution?
To use the SAM-based prior, we propose a simple yet effective module -- SAM-guidEd refinEment Module (SEEM), which can enhance both alignment and fusion procedures by the utilization of semantic information.
Expanding Synthetic Real-World Degradations for Blind Video Super Resolution
Video super-resolution (VSR) techniques, especially deep-learning-based algorithms, have drastically improved over the last few years and shown impressive performance on synthetic data.
