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ICASSP
2011
IEEE
13 years 10 days ago
Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
Hanchao Qi, Shannon Hughes
CORR
2011
Springer
186views Education» more  CORR 2011»
13 years 4 days ago
Blind Compressed Sensing Over a Structured Union of Subspaces
—This paper addresses the problem of simultaneous signal recovery and dictionary learning based on compressive measurements. Multiple signals are analyzed jointly, with multiple ...
Jorge Silva, Minhua Chen, Yonina C. Eldar, Guiller...
CORR
2010
Springer
114views Education» more  CORR 2010»
13 years 8 months ago
Sequential Compressed Sensing
Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of random measurements. Existing results in compressed sens...
Dmitry M. Malioutov, Sujay Sanghavi, Alan S. Wills...
ICIP
2007
IEEE
14 years 10 months ago
An Efficient Method for Compressed Sensing
Compressed sensing or compressive sampling (CS) has been receiving a lot of interest as a promising method for signal recovery and sampling. CS problems can be cast as convex prob...
Seung-Jean Kim, Kwangmoo Koh, Michael Lustig, Step...
ICIP
2009
IEEE
14 years 9 months ago
Dequantizing Compressed Sensing With Non-gaussian Constraints
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 ...