Joint Sparsity With Partially Known Support and Application to Ultrasound Imaging
Joint Sparsity With Partially Known Support and Application to Ultrasound Imaging
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DOI：
10.1109/lsp.2018.2880571
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发表时间：
2019-01
影响因子：
3.9
通讯作者：
Adrien Besson;Dimitris Perdios;Y. Wiaux;J. Thiran
中科院分区：
文献类型：
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作者：
Adrien Besson;Dimitris Perdios;Y. Wiaux;J. Thiran
我们调查的好处，已知的部分支持联合稀疏信号的恢复，并证明这是有利的秩盲和秩感知算法的恢复性能。我们建议几个联合稀疏恢复算法的扩展，例如，同时归一化迭代硬阈值，子空间贪婪方法和子空间增强多信号分类技术。我们描述了一个直接应用所提出的方法压缩复用的超声（US）信号。该技术利用压缩复用器架构进行信号压缩，并依赖于US信号在频域中的联合稀疏性进行信号重构。我们验证了所提出的算法的数值实验，并显示其优越性对国家的最先进的方法在秩缺陷的情况下。我们还表明，该技术导致显着增加的图像质量在体内颈动脉图像相比，重建没有部分已知的支持。支持代码可在https://github.com/AdriBesson/spl2018_joint_sparse上获得。
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.