Multi-frame image super resolution based on sparse coding

Toshiyuki Kato, Hideitsu Hino*, Noboru Murata

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and sparse signal representation for estimating the corresponding high-resolution image, where correspondence between high- and low-resolution images are modeled by a certain degradation process. Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method. The matching scores of the block matching are used to select a subset of low-resolution patches for reconstructing a high-resolution patch, that is, an adaptive selection of informative low-resolution images is realized. The proposed method is shown to perform comparable or superior to conventional super-resolution methods through experiments using various images.

Original languageEnglish
Pages (from-to)64-78
Number of pages15
JournalNeural Networks
Publication statusPublished - 2015 Jun 1


  • Image super resolution
  • Multi-frame super-resolution
  • Sparse coding

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


Dive into the research topics of 'Multi-frame image super resolution based on sparse coding'. Together they form a unique fingerprint.

Cite this