Video super-resolution is the task of upscaling a video from a low-resolution to a high-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.
In our work, we instead propose an adversarial training for video super-resolution that leads to temporally coherent solutions without sacrificing spatial detail.
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.
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In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.
In this paper, we show that proper frame alignment and motion compensation is crucial for achieving high quality results.
#6 best model for Video Super-Resolution on Vid4 - 4x upscaling
Many video enhancement algorithms rely on optical flow to register frames in a video sequence.
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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.
Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images.
#3 best model for Video Super-Resolution on Vid4 - 4x upscaling