Multi-frame image super resolution based on sparse coding

Toshiyuki Kato, Hideitsu Hino, Noboru Murata

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

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
Volume66
DOIs
Publication statusPublished - 2015 Jun 1

Keywords

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

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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