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Found 4 papers, 2 papers with code
Error Compensation Framework for Flow-Guided Video Inpainting
The key to video inpainting is to use correlation information from as many reference frames as possible.
Deep Space-Time Video Upsampling Networks
Video super-resolution (VSR) and frame interpolation (FI) are traditional computer vision problems, and the performance have been improving by incorporating deep learning recently.
Learning the Loss Functions in a Discriminative Space for Video Restoration
Our framework is similar to GANs in that we iteratively train two networks - a generator and a loss network.
Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation
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.
Ranked #6 on
Video Super-Resolution
on Vid4 - 4x upscaling
