3 papers
cs.CV2017
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks
Joost van Amersfoort, Wenzhe Shi, Alejandro Acosta +4
Frame interpolation attempts to synthesise frames given one or more consecutive video frames. In recent years, deep learning approaches, and notably convolutional neural networks,…
cs.CV2016
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Jose Caballero, Christian Ledig, Andrew Aitken +4
Convolutional neural networks have enabled accurate image super-resolution in real-time. However, recent attempts to benefit from temporal correlations in video super-resolution ha…
cs.CV2016
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig, Lucas Theis, Ferenc Huszar +8
Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved…