Optoelectronics Letters

, Volume 13, Issue 6, pp 439–443 | Cite as

Application of regularization technique in image super-resolution algorithm via sparse representation

  • De-tian Huang (黄德天)
  • Wei-qin Huang (黄炜钦)
  • Hui Huang (黄辉)
  • Li-xin Zheng (郑力新)
Article

Abstract

To make use of the prior knowledge of the image more effectively and restore more details of the edges and structures, a novel sparse coding objective function is proposed by applying the principle of the non-local similarity and manifold learning on the basis of super-resolution algorithm via sparse representation. Firstly, the non-local similarity regularization term is constructed by using the similar image patches to preserve the edge information. Then, the manifold learning regularization term is constructed by utilizing the locally linear embedding approach to enhance the structural information. The experimental results validate that the proposed algorithm has a significant improvement compared with several super-resolution algorithms in terms of the subjective visual effect and objective evaluation indices.

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Copyright information

© Tianjin University of Technology and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • De-tian Huang (黄德天)
    • 1
    • 2
    • 3
  • Wei-qin Huang (黄炜钦)
    • 1
  • Hui Huang (黄辉)
    • 2
  • Li-xin Zheng (郑力新)
    • 1
    • 3
  1. 1.College of EngineeringHuaqiao UniversityQuanzhouChina
  2. 2.College of Mechanical Engineering and AutomationHuaqiao UniversityXiamenChina
  3. 3.University Engineering Research Center of Fujian Province Industrial Intelligent Technology and SystemsHuaqiao UniversityQuanzhouChina

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