Fast search of a similar patch for self-similarity based image super resolution

Jun Sang Yoo, Ji Hoon Choi, Kang Sun Choi, Dae Yeol Lee, Hui Yong Kim, Jong-Ok Kim

Research output: Contribution to journalArticle

Abstract

In the self-similarity super resolution (SR) approach, similar examples are searched across down-scales in the image pyramid, and the computations of searching similar examples are very heavy. This makes it difficult to work in a real-time way under common software implementation. Therefore, the search process should be further accelerated at an algorithm level. Cauchy-Schwarz inequality has been used previously for fast vector quantization (VQ) encoding. The candidate patches in the search region of SR are analogous to the code-words in the VQ, and Cauchy- Schwarz inequality is exploited to exclude implausible candidate patches early. Consequently, significant acceleration of the similar patch search process is achieved. The proposed method can easily make an optimal trade-off between running speed and visual quality by appropriately configuring the bypass-threshold.

Original languageEnglish
Pages (from-to)2194-2198
Number of pages5
JournalIEICE Transactions on Information and Systems
VolumeE99D
Issue number8
DOIs
Publication statusPublished - 2016 Aug 1

Keywords

  • Fast search
  • Kick-out condition
  • Self-similarity
  • Super-resolution

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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