Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


Video super-resolution (VSR) has become one of the most critical problems in video processing. In the deep learning literature, recent works have shown the benefits of using adversarial-based and perceptual losses to improve the performance on various image restoration tasks; however, these have yet to be applied for video super-resolution. In this paper, we propose a generative adversarial network (GAN)-based formulation for VSR. We introduce a new generator network optimized for the VSR problem, named VSRResNet, along with new discriminator architecture to properly guide VSRResNet during the GAN training. We further enhance our VSR GAN formulation with two regularizers, a distance loss in feature-space and pixel-space, to obtain our final VSRResFeatGAN model. We show that pre-training our generator with the mean-squared-error loss only quantitatively surpasses the current state-of-the-art VSR models. Finally, we employ the PercepDist metric to compare the state-of-the-art VSR models. We show that this metric more accurately evaluates the perceptual quality of SR solutions obtained from neural networks, compared with the commonly used PSNR/SSIM metrics. Finally, we show that our proposed model, the VSRResFeatGAN model, outperforms the current state-of-the-art SR models, both quantitatively and qualitatively.

Original languageEnglish (US)
Article number8629024
Pages (from-to)3312-3327
Number of pages16
JournalIEEE Transactions on Image Processing
Issue number7
StatePublished - Jul 2019


  • Artificial neural networks
  • image generation
  • image resolution
  • video signal processing

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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