title: Multi-structural signal recovery for biomedical compressive sensing.
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.
authors.forename: Yipeng
authors.initials: Y
authors.lastname: Liu
authors.forename: Maarten
authors.initials: M
authors.lastname: De Vos
authors.forename: Ivan
authors.initials: I
authors.lastname: Gligorijevic
authors.forename: Vladimir
authors.initials: V
authors.lastname: Matic
authors.forename: Yuqian
authors.initials: Y
authors.lastname: Li
authors.forename: Sabine
authors.initials: S
authors.lastname: Van Huffel
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D008991:Monitoring, Physiologic / Q000379:methods*