Single image super-resolution based on space structure learning

Heng Su, Nan Jiang, Ying Wu, Jie Zhou*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In this paper, the learning-based single image super-resolution (SR) is regarded as a problem of space structure learning. We propose a new SR method that identifies a space from the low-resolution (LR) image space that best preserves the structure of the high-resolution (HR) image space. The inference between the two structure-consistent spaces proves to be accurate and predicts HR image patches with higher quality. An effective iterative algorithm is also proposed to find the near-optimal solution to the model, which can be easily implemented in parallel computing. Extensive experiments are performed to show the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Pages (from-to)2094-2101
Number of pages8
JournalPattern Recognition Letters
Issue number16
StatePublished - 2013


  • Metric learning
  • Single image super-resolution
  • Space structure learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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