Robust Single-Image Super-Resolution via CNNs and TV-TV Minimization

Marija Vella, João F. C. Mota

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
7 Downloads (Pure)


Single-image super-resolution is the process of increasing the resolution of an image, obtaining a high-resolution (HR) image from a low-resolution (LR) one. By leveraging large training datasets, convolutional neural networks (CNNs) currently achieve the state-of-the-art performance in this task. Yet, during testing/deployment, they fail to enforce consistency between the HR and LR images: if we downsample the output HR image, it never matches its LR input. Based on this observation, we propose to post-process the CNN outputs with an optimization problem that we call TV-TV minimization, which enforces consistency. As our extensive experiments show, such post-processing not only improves the quality of the images, in terms of PSNR and SSIM, but also makes the super-resolution task robust to operator mismatch, i.e., when the true downsampling operator is different from the one used to create the training dataset.

Original languageEnglish
Pages (from-to)7830-7841
Number of pages12
JournalIEEE Transactions on Image Processing
Early online date10 Sep 2021
Publication statusPublished - 2021


  • Image super-resolution
  • convolutional neural networks. (CNNs)
  • image reconstruction
  • prior information
  • ℓ ℓ minimization

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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