Non-local total bounded variation scheme for multiple-coil magnetic resonance image restoration View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-07-12

AUTHORS

P. Jidesh, Shivaram Holla

ABSTRACT

In this paper, we design a variational model for restoring multiple-coil magnetic resonance images (MRI) corrupted by non-central Chi distributed noise. The energy functional corresponding to the restoration problem is derived using the maximum a posteriori (MAP) estimator. Optimizing this functional yields the solution, which corresponds to the restored version of the image. The non-local total bounded variation prior is being used as the regularization term in the functional derived using the MAP estimation process. Further, the split-Bregman iteration scheme is being followed for fast numerical computation of the model. The results are compared with the state of the art MRI restoration models using visual representations and statistical measures. More... »

PAGES

1427-1448

References to SciGraph publications

  • 2012-05-10. A coupled variational model for image denoising using a duality strategy and split Bregman in MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
  • 2009-11-05. Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction in JOURNAL OF SCIENTIFIC COMPUTING
  • 2005. Nonparametric Neighborhood Statistics for MRI Denoising in INFORMATION PROCESSING IN MEDICAL IMAGING
  • 2014-02-12. Split Bregman algorithms for sparse group Lasso with application to MRI reconstruction in MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
  • Identifiers

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    http://scigraph.springernature.com/pub.10.1007/s11045-017-0510-z

    DOI

    http://dx.doi.org/10.1007/s11045-017-0510-z

    DIMENSIONS

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