Sparse representation-based multiple frame video super-resolution

Qiqin Dai*, Seunghwan Yoo, Armin Kappeler, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

In this paper, we propose two multiple-frame super-resolution (SR) algorithms based on dictionary learning (DL) and motion estimation. First, we adopt the use of video bilevel DL, which has been used for single-frame SR. It is extended to multiple frames by using motion estimation with sub-pixel accuracy. We propose a batch and a temporally recursive multi-frame SR algorithm, which improves over single-frame SR. Finally, we propose a novel DL algorithm utilizing consecutive video frames, rather than still images or individual video frames, which further improves the performance of the video SR algorithms. Extensive experimental comparisons with the state-of-the-art SR algorithms verify the effectiveness of our proposed multiple-frame video SR approach.

Original languageEnglish (US)
Article number7752984
Pages (from-to)765-781
Number of pages17
JournalIEEE Transactions on Image Processing
Volume26
Issue number2
DOIs
StatePublished - Feb 2017

Keywords

  • Video super-resolution
  • dictionary learning
  • motion estimation
  • optical flow
  • sparse coding

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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