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

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 method sand subspace-augmented multiple signal classification (MUSIC)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.
LanguageEnglish
Pages84-88
Number of pages5
JournalIEEE Signal Processing Letters
Volume26
Issue number1
Early online date12 Nov 2018
DOIs
StatePublished - Jan 2019

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Ultrasonics
Imaging techniques
Recovery
Signal reconstruction
Multiplexing
Image quality
Sand
Experiments

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Besson, Adrien ; Perdios, Dimitris ; Wiaux, Yves ; Thiran, Jean-Philippe. / Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging. In: IEEE Signal Processing Letters. 2019 ; Vol. 26, No. 1. pp. 84-88
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Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging. / Besson, Adrien; Perdios, Dimitris; Wiaux, Yves; Thiran, Jean-Philippe.

In: IEEE Signal Processing Letters, Vol. 26, No. 1, 01.2019, p. 84-88.

Research output: Contribution to journalArticle

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