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ICIP
2008
IEEE
14 years 10 months ago
Kalman filtered Compressed Sensing
We consider the problem of reconstructing time sequences of spatially sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear "incohe...
Namrata Vaswani
CISS
2008
IEEE
14 years 3 months ago
Reconstruction of compressively sensed images via neurally plausible local competitive algorithms
Abstract—We develop neurally plausible local competitive algorithms (LCAs) for reconstructing compressively sensed images. Reconstruction requires solving a sparse approximation ...
Robert L. Ortman, Christopher J. Rozell, Don H. Jo...
DCC
2007
IEEE
14 years 8 months ago
Quantization of Sparse Representations
Compressive sensing (CS) is a new signal acquisition technique for sparse and compressible signals. Rather than uniformly sampling the signal, CS computes inner products with rand...
Petros Boufounos, Richard G. Baraniuk
TSP
2008
124views more  TSP 2008»
13 years 9 months ago
Dictionary Preconditioning for Greedy Algorithms
This article introduces the concept of sensing dictionaries. It presents an alteration of greedy algorithms like thresholding or (Orthogonal) Matching Pursuit which improves their...
Karin Schnass, Pierre Vandergheynst
CORR
2010
Springer
128views Education» more  CORR 2010»
13 years 9 months ago
Blind Compressed Sensing
The fundamental principle underlying compressed sensing is that a signal, which is sparse under some basis representation, can be recovered from a small number of linear measuremen...
Sivan Gleichman, Yonina C. Eldar