Generative adversarial networks and perceptual losses for video super-resolution

Alice Lucas, Aggelos K. Katsaggelos, Santiago Lopez-Tapuia, Rafael Molina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Recent research on image super-resolution (SR) has shown that the use of perceptual losses such as feature-space loss functions and adversarial training can greatly improve the perceptual quality of the resulting SR output. In this paper, we extend the use of these perceptual-focused approaches for image SR to that of video SR. We design a 15-block residual neural network, VSRResNet, which is pre-trained on a the traditional mean -squared -error (MSE) loss and later fine-tuned with a feature-space loss function in an adversarial setting. We show that our proposed system, VSRRes-FeatGAN, produces super-resolved frames of much higher perceptual quality than those provided by the MSE-based model.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages51-55
Number of pages5
ISBN (Electronic)9781479970612
DOIs
StatePublished - Aug 29 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: Oct 7 2018Oct 10 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
CountryGreece
CityAthens
Period10/7/1810/10/18

Keywords

  • Convolutional Neuronal Networks
  • Generative Adversarial Networks
  • Perceptual Loss Functions
  • Superresolution
  • Video

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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