Image and video upscaling from local self-examples

Gilad Freedman*, Raanan Fattal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

519 Scopus citations

Abstract

We propose a new high-quality and efficient single-image upscaling technique that extends existing example-based super-resolution frameworks. In our approach we do not rely on an external example database or use thewhole input image as a source for example patches. Instead, we follow a local self-similarity assumption on natural images and extract patches from extremely localized regions in the input image. This allows us to reduce considerably the nearest-patch search time without compromising quality in most images. Tests, that we perform and report, show that the local self-similarity assumption holds better for small scaling factors where there are more example patches of greater relevance. We implement these small scalings using dedicated novel nondyadic filter banks, that we derive based on principles that model the upscaling process. Moreover, the newfilters are nearly biorthogonal and hence produce high-resolution images that are highly consistent with the input image without solving implicit back-projection equations. The local and explicit nature of our algorithm makes it simple, efficient, and allows a trivial parallel implementation on a GPU. We demonstrate the new method ability to produce high-quality resolution enhancement, its application to video sequences with no algorithmic modification, and its efficiency to perform real-time enhancement of low-resolution video standard into recent high-definition formats.

Original languageAmerican English
Article number12
JournalACM Transactions on Graphics
Volume30
Issue number2
DOIs
StatePublished - Apr 2011

Keywords

  • Image and video upscaling
  • Natural image modeling
  • Nondyadic filter banks
  • Scale invariance
  • Superresolution
  • Wavelets

Fingerprint

Dive into the research topics of 'Image and video upscaling from local self-examples'. Together they form a unique fingerprint.

Cite this