CORR
2010
Springer
13 years 1 months ago
2010
Springer
Recently, the statistical restricted isometry property (RIP) has been formulated to analyze the performance of deterministic sampling matrices for compressed sensing. In this paper...
ICIP
2009
IEEE
13 years 2 months ago
2009
IEEE
The theory of compressed sensing tells a dramatic story that sparse signals can be reconstructed near-perfectly from a small number of random measurements. However, recent work ha...
ICIP
2009
IEEE
13 years 2 months ago
2009
IEEE
Leading compressed sensing (CS) methods require m = O (k log(n)) compressive samples to perfectly reconstruct a k-sparse signal x of size n using random projection matrices (e.g., ...
PERCOM
2010
ACM
13 years 2 months ago
2010
ACM
—Data loss in wireless communications greatly affects the reconstruction quality of a signal. In the case of images, data loss results in a reduction in quality of the received i...
ICMCS
2010
IEEE
13 years 3 months ago
2010
IEEE
In a multiview-imaging setting, image-acquisition costs could be substantially diminished if some of the cameras operate at a reduced quality. Compressed sensing is proposed to ef...
FOCM
2010
13 years 3 months ago
2010
In compressed sensing we seek to gain information about vector x ∈ RN from d << N nonadaptive linear measurements. Candes, Donoho, Tao et. al. ( see e.g. [2, 4, 8]) propos...
CORR
2010
Springer
13 years 3 months ago
2010
Springer
This paper surveys recent work in applying ideas from graphical models and message passing algorithms to solve large scale regularized regression problems. In particular, the focu...
JC
2007
13 years 4 months ago
2007
Compressed sensing is a new area of signal processing. Its goal is to minimize the number of samples that need to be taken from a signal for faithful reconstruction. The performan...
CORR
2007
Springer
13 years 4 months ago
2007
Springer
This article extends the concept of compressed sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, whic...
CORR
2010
Springer
13 years 4 months ago
2010
Springer
Spectral clustering is one of the most widely used techniques for extracting the underlying global structure of a data set. Compressed sensing and matrix completion have emerged a...