Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI

  • Guang YangEmail author
  • Xujiong Ye
  • Greg Slabaugh
  • Jennifer Keegan
  • Raad Mohiaddin
  • David Firmin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9680)


Super-resolved image enhancement is of great importance in medical imaging. Conventional methods often require multiple low resolution (LR) images from different views of the same object or learning from large amount of training datasets to achieve success. However, in real clinical environments, these prerequisites are rarely fulfilled. In this paper, we present a self-learning based method to perform super-resolution (SR) from a single LR input. The mappings between the given LR image and its downsampled versions are modeled using support vector regression on features extracted from sparse coded dictionaries, coupled with dual-tree complex wavelet transform based denoising. We demonstrate the efficacy of our method in application of cardiac MRI enhancement. Both quantitative and qualitative results show that our SR method is able to preserve fine textural details that can be corrupted by noise, and therefore can maintain crucial diagnostic information.


Late Gadolinium Enhancement Discrete Wavelet Transform Support Vector Regression Support Vector Regression Model Bicubic Interpolation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Guang Yang
    • 1
    • 2
    Email author
  • Xujiong Ye
    • 3
  • Greg Slabaugh
    • 4
  • Jennifer Keegan
    • 1
    • 2
  • Raad Mohiaddin
    • 1
    • 2
  • David Firmin
    • 1
    • 2
  1. 1.Cardiovascular MR UnitRoyal Brompton HospitalLondonUK
  2. 2.National Heart & Lung InstituteImperial College LondonLondonUK
  3. 3.School of Computer ScienceUniversity of LincolnLincolnUK
  4. 4.Department of Computer ScienceCity University LondonLondonUK

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