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115
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ICASSP
2010
IEEE
15 years 28 days ago
Adaptive compressed sensing - A new class of self-organizing coding models for neuroscience
Sparse coding networks, which utilize unsupervised learning to maximize coding efficiency, have successfully reproduced response properties found in primary visual cortex [1]. Ho...
William K. Coulter, Cristopher J. Hillar, Guy Isle...
131
Voted
ICUMT
2009
14 years 10 months ago
A Bayesian analysis of Compressive Sensing data recovery in Wireless Sensor Networks
Abstract--In this paper we address the task of accurately reconstructing a distributed signal through the collection of a small number of samples at a data gathering point using Co...
Riccardo Masiero, Giorgio Quer, Michele Rossi, Mic...
82
Voted
CORR
2010
Springer
97views Education» more  CORR 2010»
14 years 10 months ago
On the Scaling Law for Compressive Sensing and its Applications
1 minimization can be used to recover sufficiently sparse unknown signals from compressed linear measurements. In fact, exact thresholds on the sparsity, as a function of the ratio...
Weiyu Xu, Ao Tang
150
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ICASSP
2011
IEEE
14 years 4 months ago
Multi image super resolution using compressed sensing
In this paper we present a new compressed sensing model and reconstruction method for multi-detector signal acquisition. We extend the concept of the famous single-pixel camera to...
Torsten Edeler, Kevin Ohliger, Stephan Hussmann, A...
70
Voted
ISBI
2009
IEEE
15 years 7 months ago
Fast Algorithms for Nonconvex Compressive Sensing: MRI Reconstruction from Very Few Data
Compressive sensing is the reconstruction of sparse images or signals from very few samples, by means of solving a tractable optimization problem. In the context of MRI, this can ...
Rick Chartrand