Learning Super-Resolution Jointly From External and Internal Examples

Zhangyang Wang, Yingzhen Yang, Zhaowen Wang, Shiyu Chang, Jianchao Yang, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review


Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from a low-resolution (LR) input. Image priors are commonly learned to regularize the, otherwise, seriously ill-posed SR problem, either using external LR-HR pairs or internal similar patterns. We propose joint SR to adaptively combine the advantages of both external and internal SR methods. We define two loss functions using sparse coding-based external examples, and epitomic matching based on internal examples, as well as a corresponding adaptive weight to automatically balance their contributions according to their reconstruction errors. Extensive SR results demonstrate the effectiveness of the proposed method over the existing state-of-the-art methods, and is also verified by our subjective evaluation study.

Original languageEnglish (US)
Article number7180353
Pages (from-to)4359-4371
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number11
StatePublished - Nov 1 2015


  • Super-resolution
  • epitome
  • example-based methods
  • sparse coding

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Learning Super-Resolution Jointly From External and Internal Examples'. Together they form a unique fingerprint.

Cite this