joint_sparse_algorithms-master

说明：  我们描述了所提出的方法对超声（US）信号的压缩多路复用的直接应用。该技术利用压缩多路复用器架构进行信号压缩，并依靠频域中US信号的联合稀疏性进行信号重建。由于换能器元件具有压电特性，因此可以获得有关US信号频率支持的准确先验知识，并且可以在联合稀疏算法中使用。 我们在数值实验中验证了所提出的方法，并显示了它们在秩次缺陷情况下相对于最新方法的优越性。我们还证明，与没有已知支持的重建相比，该技术可显着提高体内颈动脉图像的图像质量。(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. Due to piezo-electric properties of transducer elements, accurate prior knowledge of the frequency support of US signals is available and can be used in joint-sparse algorithms. We validate the proposed methods 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 known support.)