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
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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
Most implemented papers
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames).
Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects
rozumden/deblatting_python
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CVPR 2020
We propose a novel method that tracks fast moving objects, mainly non-uniform spherical, in full 6 degrees of freedom, estimating simultaneously their 3D motion trajectory, 3D pose and object appearance changes with a time step that is a fraction of the video frame exposure time.
Deep Video Super-Resolution using HR Optical Flow Estimation
The key challenge for video SR lies in the effective exploitation of temporal dependency between consecutive frames.
Deep Blind Video Super-resolution
csbhr/Deep-Blind-VSR
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ICCV 2021
Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration.
Real-World Super-Resolution via Kernel Estimation and Noise Injection
Recent state-of-the-art super-resolution methods have achieved impressive performance on ideal datasets regardless of blur and noise.
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
EscVM/RAMS
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Convolutional Neural Networks (CNNs) have been consistently proved state-of-the-art results in image Super-Resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data.
Video Super-Resolution with Recurrent Structure-Detail Network
Most video super-resolution methods super-resolve a single reference frame with the help of neighboring frames in a temporal sliding window.
Revisiting Temporal Modeling for Video Super-resolution
junpan19/RRN
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Video super-resolution plays an important role in surveillance video analysis and ultra-high-definition video display, which has drawn much attention in both the research and industrial communities.
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
lovepiano/SMFN
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Spherical videos, also known as \ang{360} (panorama) videos, can be viewed with various virtual reality devices such as computers and head-mounted displays.
COMISR: Compression-Informed Video Super-Resolution
google-research/google-research
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ICCV 2021
Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression.
