Dictionary Learning-Based MR Image Reconstruction in the Presence of Speckle Noise: Greedy Versus Convex

  • M. V. R. ManimalaEmail author
  • C. Dhanunjaya Naidu
  • M. N. Giri Prasad
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)


The trend of acquiring samples at a rate far below Nyquist rate is termed as compressive sensing (CS). CS enables the accurate recovery of signals/images by exploiting the underlying sparsity in either transform or signal domain. CS is useful in reducing acquisition time in MR imaging. In this paper, CS based on dictionary learning has been applied for MR imaging in the presence of speckle noise. Performance of two classes of sparse recovery techniques, namely convex optimization and greedy iterative algorithms, has been investigated and compared, when employed for the sparse coding stage of an adaptive patch-based dictionary. Two greedy algorithms, orthogonal matching pursuit (OMP) and compressive sampling matching pursuit (CoSaMP), are considered and contrasted with convex techniques: basis pursuit (BP) and least absolute shrinkage and selection operator (LASSO). Experimentation has been done on spine, knee, and brain image by varying the speckle noise variance, sparsity threshold per patch for various sampling schemes. Results show that convex techniques achieve higher peak signal-to-noise ratio (PSNR) than greedy algorithms in the presence of high noise.


Compressive sensing Orthogonal matching pursuit Compressive sampling matching pursuit Basis pursuit Least absolute shrinkage and selection operator 



The brain image and various masks used in this work are made available online by Dr. Saiprasad Ravishankar and Dr. Yoram Bresler for research purpose, and the authors would like to thank them for the same.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • M. V. R. Manimala
    • 1
    Email author
  • C. Dhanunjaya Naidu
    • 2
  • M. N. Giri Prasad
    • 1
  1. 1.JNTUAAnanthapuramuIndia
  2. 2.VNRVJIETHyderabadIndia

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