11 search results - page 2 / 3 » Distributed compressed sensing: Sparsity models and reconstr... |
ICASSP
2009
IEEE
14 years 2 months ago
2009
IEEE
In this paper we model the components of the compressive sensing (CS) problem using the Bayesian framework by utilizing a hierarchical form of the Laplace prior to model sparsity ...
ICIP
2007
IEEE
14 years 9 months ago
2007
IEEE
We introduce a new approach to image reconstruction from highly incomplete data. The available data are assumed to be a small collection of spectral coef?cients of an arbitrary li...
ICASSP
2008
IEEE
14 years 2 months ago
2008
IEEE
Compressive sensing aims to recover a sparse or compressible signal from a small set of projections onto random vectors; conventional solutions involve linear programming or greed...
ICASSP
2008
IEEE
14 years 2 months ago
2008
IEEE
The emerging theory of compressed sensing (CS) provides a universal signal detection approach for sparse signals at sub-Nyquist sampling rates. A small number of random projection...
SIAMIS
2011
13 years 2 months ago
2011
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...