Video Super-Resolution

76 papers with code • 12 benchmarks • 9 datasets

Video super-resolution is the task of upscaling a video from a low-resolution to a high-resolution.

( 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).

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.

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.

Video Enhancement with Task-Oriented Flow

anchen1011/toflow 24 Nov 2017

Many video enhancement algorithms rely on optical flow to register frames in a video sequence.

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

rozumden/DeFMO CVPR 2021

We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub-frames as if captured by a high-speed camera (i. e. temporal super-resolution).

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

xinntao/BasicSR CVPR 2021

Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension.