"Multi-Structural Signal Recovery for Biomedical Compressive Sensing," Biomedical Engineering, IEEE Transactions on , vol.PP, no.99, pp.1,1, 0 doi: 10.1109/TBME.2013.2264772 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 dierent 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.
Posted on: Wed, 07 Aug 2013 13:04:45 +0000
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