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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We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
#2 best model for Video Super-Resolution on Xiph HD - 4x upscaling
This means that the super-resolution (SR) operation is performed in HR space.
Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution.
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.
#9 best model for Video Super-Resolution on Vid4 - 4x upscaling
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.
Many video enhancement algorithms rely on optical flow to register frames in a video sequence.
#5 best model for Video Frame Interpolation on Middlebury
In this paper, we show that proper frame alignment and motion compensation is crucial for achieving high quality results.
#8 best model for Video Super-Resolution on Vid4 - 4x upscaling
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.
#3 best model for Video Super-Resolution on Vid4 - 4x upscaling