Joint Sparsity With Partially Known Support and Application to Ultrasound Imaging

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...

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Bibliographic Details
Published in:IEEE signal processing letters Vol. 26; no. 1; pp. 84 - 88
Main Authors: Besson, Adrien, Perdios, Dimitris, Wiaux, Yves, Thiran, Jean-Philippe
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Compressed sensing
Greedy algorithms
Image quality
Image reconstruction
Iterative algorithms
Iterative methods
joint sparsity
Matching pursuit algorithms
Multiple signal classification
Multiplexing
MUSIC
Recovery
Signal classification
Signal processing algorithms
Signal reconstruction
Sparks
Sparsity
State of the art
Subspace methods
Ultrasonic imaging
Ultrasonic testing
Ultrasound
ISSN:1070-9908, 1558-2361
Online Access:Get full text
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Summary: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.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2018.2880571