2013/06/27 by Yipeng Liu, Maarten De Vos, Liu, Yipeng +11
Computer Science · Engineering · Mathematics · Medicine · #A priori and a posteriori #Algorithm #Applications (stat.AP) #Compressed sensing #Computer science #Convex optimization #Domain (mathematical analysis) #Exploit #FOS: Computer and information sciences #Information Theory (cs.IT) #Mathematical optimization #Mathematics #Photoacoustic and Ultrasonic Imaging #Piecewise #Piecewise linear function #Regular polygon #SIGNAL (programming language) #Signal processing #Signal reconstruction #Smoothness #Sparse and Compressive Sensing Techniques #Ultrasound Imaging and Elastography #Underdetermined system #cs.IT #math.IT #stat.AP
paper · pdf · doi:10.48550/arxiv.1306.6510
published in arXiv (Cornell University) (Cornell University) · 29 pages, 20 figures, accepted by IEEE Transactions on Biomedical Engineering. Online first version: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6519288&tag=1
arxiv created 2013/06/27 · openalex publication_date 2013/06/27 · arxiv updated 2016/11/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Compressive sensing has shown significant promise in biomedical fields. It reconstructs a signal from sub-Nyquist random linear measurements. Classical methods only exploit the sparsity in one domain. A lot of biomedical signals have additional structures, such as multi-sparsity in different domains, piecewise smoothness, low rank, etc. We propose a framework to exploit all the available structure information. A new convex programming problem is generated with multiple convex structure-inducing constraints and the linear measurement fitting constraint. With additional a priori information for solving the underdetermined system, the signal recovery performance can be improved. In numerical experiments, we compare the proposed method with classical methods. Both simulated data and real-life biomedical data are used. Results show that the newly proposed method achieves better reconstruction accuracy performance in term of both L1 and L2 errors.