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Video Super-Resolution

24 papers with code · Computer Vision
Subtask of Video · Super-Resolution

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 )

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Image Super-Resolution Using Deep Convolutional Networks

31 Dec 2014nagadomi/waifu2x

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

IMAGE SUPER-RESOLUTION VIDEO SUPER-RESOLUTION

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

ICLR 2020 thunil/TecoGAN

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

IMAGE SUPER-RESOLUTION MOTION COMPENSATION VIDEO GENERATION VIDEO SUPER-RESOLUTION

Deep Back-Projection Networks For Super-Resolution

CVPR 2018 thstkdgus35/EDSR-PyTorch

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.

IMAGE SUPER-RESOLUTION VIDEO SUPER-RESOLUTION

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

7 May 2019xinntao/EDVR

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

DEBLURRING VIDEO SUPER-RESOLUTION

Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution

CVPR 2020 Mukosame/Zooming-Slow-Mo-CVPR-2020

Rather than synthesizing missing LR video frames as VFI networks do, we firstly temporally interpolate LR frame features in missing LR video frames capturing local temporal contexts by the proposed feature temporal interpolation network.

VIDEO FRAME INTERPOLATION VIDEO SUPER-RESOLUTION

Detail-revealing Deep Video Super-resolution

ICCV 2017 jiangsutx/SPMC_VideoSR

In this paper, we show that proper frame alignment and motion compensation is crucial for achieving high quality results.

IMAGE SUPER-RESOLUTION MOTION COMPENSATION VIDEO SUPER-RESOLUTION

Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation

CVPR 2018 yhjo09/VSR-DUF

We propose a novel end-to-end deep neural network that generates dynamic upsampling filters and a residual image, which are computed depending on the local spatio-temporal neighborhood of each pixel to avoid explicit motion compensation.

DATA AUGMENTATION MOTION COMPENSATION MOTION ESTIMATION VIDEO SUPER-RESOLUTION