Dictionary-based multiple frame video super-resolution

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations


In this paper, we propose a multiple-frame super-resolution (SR) algorithm based on dictionary learning and motion estimation. We adopt the use of multiple bilevel dictionaries which have also been used for single-frame SR. Multiple frames compensated through sub-pixel motion are considered. By simultaneously solving for a batch of patches from multiple frames, the proposed multiple-frame SR algorithm improves over single frame SR. We also propose a novel dictionary learning algorithm based on which dictionaries are trained from consecutive video frames, rather than still images or individual video frames, which further improves the performance of the developed video SR algorithm. Extensive experimental comparisons with state-of-the-art SR algorithms verifies the effectiveness of our proposed multiple-frame SR approach.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479983391
StatePublished - Dec 9 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: Sep 27 2015Sep 30 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


OtherIEEE International Conference on Image Processing, ICIP 2015
CityQuebec City


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

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


Dive into the research topics of 'Dictionary-based multiple frame video super-resolution'. Together they form a unique fingerprint.

Cite this