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Image Reconstruction Using NMF with Sparse Constraints Based on Kurtosis Measurement Criterion

  • Li Shang
  • Jinfeng Zhang
  • Wenjun Huai
  • Jie Chen
  • Jixiang Du
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5755)

Abstract

A novel image reconstruction method using non-negative matrix factorization (NMF) with sparse constraints based on the kurtosis measurement is proposed by us. This NMF algorithm with sparse constraints exploited the Kurtosis as the maximizing sparse measure criterion of feature coefficients. The experimental results show that the natural images’ feature basis vectors can be successfully extracted by using our algorithm. Furthermore, compared with the standard NMF method, the simulation results show that our algorithm is indeed efficient and effective in performing image reconstruction task.

Keywords

NMF Sparse constraints Kurtosis Image reconstruction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Li Shang
    • 1
    • 2
  • Jinfeng Zhang
    • 2
  • Wenjun Huai
    • 2
  • Jie Chen
    • 2
  • Jixiang Du
    • 3
    • 4
    • 5
  1. 1.JiangSu Province Support Software Engineering R&D Center for Modern Information Technology Application in EnterpriseSuzhouChina
  2. 2.Department of Electronic Information EngineeringSuzhou Vocational UniversitySuzhouChina
  3. 3.Department of Computer Science and TechnologyHuaqiao UniversityQuanzhouChina
  4. 4.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina
  5. 5.Institute of Intelligent MachinesChinese Academy of SciencesHefeiChina

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