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TIT
2010
174views Education» more  TIT 2010»
13 years 2 months ago
Toeplitz Compressed Sensing Matrices With Applications to Sparse Channel Estimation
Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. In essence, CS enables the recovery of high-dimensional sparse signals from relatively few ...
Jarvis Haupt, Waheed Uz Zaman Bajwa, Gil M. Raz, R...
ICASSP
2011
IEEE
12 years 11 months ago
Improved model-based spectral compressive sensing via nested least squares
This paper introduces a new algorithm for reconstructing signals with sparse spectrums from noisy compressive measurements. The proposed model-based algorithm takes the signal str...
Mahdi Shaghaghi, Sergiy A. Vorobyov
ICIP
2008
IEEE
14 years 9 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
CORR
2012
Springer
225views Education» more  CORR 2012»
12 years 3 months ago
Compressive Principal Component Pursuit
We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in co...
John Wright, Arvind Ganesh, Kerui Min, Yi Ma
ICASSP
2010
IEEE
13 years 7 months ago
Ultrasound tomography with learned dictionaries
We propose a new method for imaging sound speed in breast tissue from measurements obtained by ultrasound tomography (UST) scanners. Given the measurements, our algorithm finds a...
Ivana Tosic, Ivana Jovanovic, Pascal Frossard, Mar...