Multimedia Tools and Applications

, Volume 75, Issue 21, pp 13107–13120 | Cite as

A novel regularized K-SVD dictionary learning based medical image super-resolution algorithm

  • Jingjing Yang
  • Xiao ZhangEmail author
  • Wei Peng
  • Zhanbiao Liu


Recently sparse representations over learned dictionaries have been proven to be a very successful representation method for many image processing applications. In the medical image processing community, image super-resolution has been playing a vital role to make the computer based diagnosis more efficient and accurate. Resolution enhancement through conventional interpolation methods strongly affects the precision of consequent processing steps such as segmentation and registration. In this paper, we propose a novel regularized K-SVD dictionary learning based medical image super-resolution algorithm. First, the dictionary is trained using the modified version of the K-SVD dictionary learning procedure. The sparse coding phase of the K-SVD dictionary learning scheme is then enhanced incorporating a simple and an efficient regularized version of orthogonal matching pursuit. In addition, the dictionary update stage allows for an arbitrary number of atoms at the same time and sparse coefficient vector. In the SR reconstruction procedure, ROMP is adopted to find out for the vector of sparse representation coefficients for the underlying patch. In the final part, mathematical optimization finalizes the SR work effectively. The numerical analysis and experimental simulation prove the feasibility and robustness of our proposed methodology compared with other state-of-the-art algorithms.


Medical image super-resolution K-SVD dictionary learning RMOP Sparse presentation Mathematical optimization 



Foundation item: The Major Programs of Hebei North University (No.ZD201301 and No.ZD201302), The Youth Foundation of the Education Department of Hebei Province (No.QN2015225) and Engineering Research Center of Population Health Information, Hebei Province. Authors are grateful to the Hebei North University for financial support to carry out this work.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Jingjing Yang
    • 1
  • Xiao Zhang
    • 1
    Email author
  • Wei Peng
    • 1
  • Zhanbiao Liu
    • 2
  1. 1.School of Information Science and EngineeringHebei North UniversityZhangjiakouChina
  2. 2.The 251ST Hospital of PLAZhangjiakouChina

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