Multiple-degradation video super-resolution with direct inversion of the low-resolution formation model

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

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

Abstract

With the increase of popularity of high and ultra high definition displays, the need to improve the quality of content already obtained at much lower resolutions has grown. Since current video super-resolution methods are trained with a single degradation model (usually bicubic downsampling), they are not robust to mismatch between training and testing degradation models, in which case their performance deteriorates. In this work we propose a new Convolutional Neural Network for video super resolution which is robust to multiple degradation models and uses the pseudo-inverse image formation model as part of the network architecture during training. The experimental validation shows that our approach outperforms current state of the art methods.

Original languageEnglish (US)
Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Electronic)9789082797039
DOIs
StatePublished - Sep 2019
Event27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain
Duration: Sep 2 2019Sep 6 2019

Publication series

NameEuropean Signal Processing Conference
Volume2019-September
ISSN (Print)2219-5491

Conference

Conference27th European Signal Processing Conference, EUSIPCO 2019
CountrySpain
CityA Coruna
Period9/2/199/6/19

Keywords

  • Convolutional neuronal networks
  • Image formation
  • Video Super-resolution

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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