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

Math @ Duke



Publications [#304072] of Guillermo Sapiro

Papers Published

  1. Duarte-Carvajalino, J; Sapiro, G; Yu, G; Carin, L, Online Adaptive Statistical Compressed Sensing of Gaussian Mixture Models (December, 2011) [1112.5895v1]
    (last updated on 2017/12/17)

    A framework of online adaptive statistical compressed sensing is introduced for signals following a mixture model. The scheme first uses non-adaptive measurements, from which an online decoding scheme estimates the model selection. As soon as a candidate model has been selected, an optimal sensing scheme for the selected model continues to apply. The final signal reconstruction is calculated from the ensemble of both the non-adaptive and the adaptive measurements. For signals generated from a Gaussian mixture model, the online adaptive sensing algorithm is given and its performance is analyzed. On both synthetic and real image data, the proposed adaptive scheme considerably reduces the average reconstruction error with respect to standard statistical compressed sensing that uses fully random measurements, at a marginally increased computational complexity.
ph: 919.660.2800
fax: 919.660.2821

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