Super-resolution via transform-invariant group-sparse regularization

Carlos Fernandez-Granda, Emmanuel J. Candes

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


We present a framework to super-resolve planar regions found in urban scenes and other man-made environments by taking into account their 3D geometry. Such regions have highly structured straight edges, but this prior is challenging to exploit due to deformations induced by the projection onto the imaging plane. Our method factors out such deformations by using recently developed tools based on convex optimization to learn a transform that maps the image to a domain where its gradient has a simple group-sparse structure. This allows to obtain a novel convex regularizer that enforces global consistency constraints between the edges of the image. Computational experiments with real images show that this data-driven approach to the design of regularizers promoting transform-invariant group sparsity is very effective at high super-resolution factors. We view our approach as complementary to most recent super-resolution methods, which tend to focus on hallucinating high-frequency textures.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Print)9781479928392
StatePublished - 2013
Event2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australia
Duration: Dec 1 2013Dec 8 2013

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


Other2013 14th IEEE International Conference on Computer Vision, ICCV 2013
CitySydney, NSW


  • Super-resolution
  • camera projection
  • convex optimization
  • deblurring
  • group sparsity
  • low-rank textures
  • transform invariance

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Super-resolution via transform-invariant group-sparse regularization'. Together they form a unique fingerprint.

Cite this