Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

Adrien Besson, Dimitris Perdios, Yves Wiaux, Jean-Philippe Thiran

Research output: Contribution to journalArticle

2 Citations (Scopus)
13 Downloads (Pure)

Abstract

We investigate the benefits of known partial support for the recovery of joint-sparse signals and demonstrate that it is advantageous in terms of recovery performance for both rank-blind and rank-aware algorithms. We suggest extensions of several joint-sparse recovery algorithms, e.g., simultaneous normalized iterative hard thresholding, subspace greedy methods and subspace-augmented multiple signal classification techniques. We describe a direct application of the proposed methods for compressive multiplexing of ultrasound (US) signals. The technique exploits the compressive multiplexer architecture for signal compression and relies on joint-sparsity of US signals in the frequency domain for signal reconstruction. We validate the proposed algorithms on numerical experiments and show their superiority against state-of-the-art approaches in rank-defective cases. We also demonstrate that the techniques lead to a significant increase of the image quality on in vivo carotid images compared to reconstruction without partially known support. The supporting code is available on https://github.com/AdriBesson/spl2018_joint_sparse.
Original languageEnglish
Pages (from-to)84-88
Number of pages5
JournalIEEE Signal Processing Letters
Volume26
Issue number1
Early online date12 Nov 2018
DOIs
Publication statusPublished - Jan 2019

Keywords

  • Compressed sensing
  • MUSIC
  • greedy algorithms
  • joint sparsity
  • ultrasound

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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