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Sparse Representation Based Super-Resolution of MRI Images with Non-Local Total Variation Regularization

  • Bhabesh DekaEmail author
  • Helal Uddin Mullah
  • Sumit Datta
  • Vijaya Lakshmi
  • Rajarajeswari Ganesan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11942)

Abstract

Diffusion-weighted and Spectroscopic MR images are found to be very helpful for diagnostic purposes as they provide complementary information to that provided by conventional MRI. These images are also acquired at a faster rate, but with low signal-to-noise ratio. This limitation can be overcome by applying image super-resolution techniques. In this paper, we propose a single-image super-resolution (SISR) technique via sparse representation for diffusion-weighted (DW) and spectroscopic MR (MRS) images. It is based on non-local total variation approach to regularize an ill-posed inverse problem of SISR. Experiments are conducted for both DW and MRS test images and the results are compared with other recent regularization-based methods using sparse representation. The comparison also validates the potential of the proposed method for clinical applications.

Keywords

Super-resolution NLTV regularization DW MRI MRSI Sparse representation 

Notes

Acknowledgements

Authors would like to thank All India Council for Technical Education (AICTE) for providing funds under the project (File No. 8-15/RIFD/RPS/POLICY-1/2016-17) and Ministry of Electronics and Information Technology (MeiTY), GoI for providing financial support under the Visvesvaraya Ph.D. Scheme for Electronics & IT (Ph.D./MLA/ 04(41)/2015-16/01) which helped in smooth conduction of the above research work.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bhabesh Deka
    • 1
    Email author
  • Helal Uddin Mullah
    • 1
  • Sumit Datta
    • 1
  • Vijaya Lakshmi
    • 1
  • Rajarajeswari Ganesan
    • 1
  1. 1.Department of Electronics and Communication EngineeringTezpur UniversityTezpurIndia

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