2026-05-30T14:55:29.976Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:5833 2018-04-26T11:53:56Z 2

Multi-structural signal recovery for biomedical compressive sensing 2013 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/5833 https://ieeexplore.ieee.org/document/6519288/ Y. Liu V. Matić Abstract: 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. article 10.1109/TBME.2013.2264772 60 10 2794 2805 0018-9294 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING