News & Updates
Search Research Content
Resource Finder at Kennedy Krieger Institute
A free resource that provides access to information and support for individuals and families living with developmental disabilities.
Multi-structural signal recovery for biomedical compressive sensing.
|Title||Multi-structural signal recovery for biomedical compressive sensing.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Liu Y, De Vos M, Gligorijevic I, Matic V, Li Y, Huffel SV|
|Journal||IEEE transactions on bio-medical engineering|
|Date Published||2013 Oct|
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.
|Alternate Journal||IEEE Trans Biomed Eng|