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.
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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.
Many video enhancement algorithms rely on optical flow to register frames in a video sequence.
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In this paper, we show that proper frame alignment and motion compensation is crucial for achieving high quality results.
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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