Abstract

In recent years, dictionary learning approaches have been used in image super-resolution, achieving promising results. Such approaches train a dictionary from image patches and reconstruct a new patch by sparse combination of the atoms of the dictionary. Typical training methods do not constrain the dictionary atoms. In this paper, we propose a convex dictionary learning (CDL) algorithm by constraining the dictionary atoms to be formed by non-negative linear combination of the training data, which is a natural, desired property. We evaluate our approach by demonstrating its performance gain over typical approaches.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages4058-4062
Number of pages5
Volume2017-September
ISBN (Electronic)9781509021758
DOIs
StatePublished - Feb 20 2018
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: Sep 17 2017Sep 20 2017

Other

Other24th IEEE International Conference on Image Processing, ICIP 2017
CountryChina
CityBeijing
Period9/17/179/20/17

Keywords

  • Dictionary learning
  • Sparse representation
  • Super-resolution

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Ding, P. L. K., Li, B., & Chang, K. (2018). Convex dictionary learning for single image super-resolution. In 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings (Vol. 2017-September, pp. 4058-4062). IEEE Computer Society. https://doi.org/10.1109/ICIP.2017.8297045