Video Super-Resolution

112 papers with code • 15 benchmarks • 12 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 )


Use these libraries to find Video Super-Resolution models and implementations

Most implemented papers

Image Super-Resolution Using Deep Convolutional Networks

nagadomi/waifu2x 31 Dec 2014

We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

xinntao/ESRGAN 1 Sep 2018

To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN).

XGBoost: A Scalable Tree Boosting System

dmlc/xgboost 9 Mar 2016

In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges.

Deep Back-Projection Networks For Super-Resolution

thstkdgus35/EDSR-PyTorch CVPR 2018

The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs, and the non-linear mapping from those to high-resolution output.

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

thunil/TecoGAN 23 Nov 2018

Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution.

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

xinntao/EDVR 7 May 2019

In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.

SwinIR: Image Restoration Using Swin Transformer

jingyunliang/swinir 23 Aug 2021

In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

xinntao/Real-ESRGAN 22 Jul 2021

Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.