Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#265135] of Guillermo Sapiro

Papers Published

  1. Duarte-Carvajalino, JM; Yu, G; Carin, L; Sapiro, G, Adapted statistical compressive sensing: Learning to sense gaussian mixture models, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp) (October, 2012), pp. 3653-3656, IEEE, ISSN 1520-6149 [doi]
    (last updated on 2019/06/18)

    A framework for learning sensing kernels adapted to signals that follow a Gaussian mixture model (GMM) is introduced in this paper. This follows the paradigm of statistical compressive sensing (SCS), where a statistical model, a GMM in particular, replaces the standard sparsity model of classical compressive sensing (CS), leading to both theoretical and practical improvements. We show that the optimized sensing matrix outperforms random sampling matrices originally exploited both in CS and SCS. © 2012 IEEE.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320