A joint perspective towards image super-resolution: Unifying external- and self-examples

Zhangyang Wang, Zhaowen Wang, Shiyu Chang, Jianchao Yang, Thomas Huang

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

Abstract

Existing example-based super resolution (SR) methods are built upon either external-examples or self-examples. Although effective in certain cases, both methods suffer from their inherent limitation. This paper goes beyond these two classes of most common example-based SR approaches, and proposes a novel joint SR perspective. The joint SR exploits and maximizes the complementary advantages of external- and self-example based methods. We elaborate on exploitable priors for image components of different nature, and formulate their corresponding loss functions mathematically. Equipped with that, we construct a unified SR formulation, and propose an iterative joint super resolution (IJSR) algorithm to solve the optimization. Such a joint perspective approach leads to an impressive improvement of SR results both quantitatively and qualitatively.

Original languageEnglish (US)
Title of host publication2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
PublisherIEEE Computer Society
Pages596-603
Number of pages8
ISBN (Print)9781479949854
DOIs
StatePublished - 2014
Event2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States
Duration: Mar 24 2014Mar 26 2014

Publication series

Name2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Other

Other2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
CountryUnited States
CitySteamboat Springs, CO
Period3/24/143/26/14

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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