5 search results - page 1 / 1 » Eigenspace sparsity for compression and denoising |
ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
Sparsity in the eigenspace of signal covariance matrices is exploited in this paper for compression and denoising. Dimensionality reduction (DR) and quantization modules present i...
SIGPRO
2010
13 years 3 months ago
2010
This short note studies a variation of the Compressed Sensing paradigm introduced recently by Vaswani et al., i.e. the recovery of sparse signals from a certain number of linear m...
CORR
2007
Springer
13 years 4 months ago
2007
Springer
The problem of recovering the sparsity pattern of a fixed but unknown vector β∗ ∈ Rp based on a set of n noisy observations arises in a variety of settings, including subset...
ICIP
2009
IEEE
14 years 5 months ago
2009
IEEE
In this paper, following the Compressed Sensing (CS) paradigm, we study the problem of recovering sparse or compressible signals from uniformly quantized measurements. We present ...
SIAMIS
2011
12 years 11 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...