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Found 3 papers, 1 papers with code
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks
To improve the quality of synthesised intermediate video frames, our network is jointly supervised at different levels with a perceptual loss function that consists of an adversarial and two content losses.
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Convolutional neural networks have enabled accurate image super-resolution in real-time.
Ranked #11 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
Ranked #3 on
Image Super-Resolution
on VggFace2 - 8x upscaling
