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CORR
2010
Springer
210views Education» more  CORR 2010»
14 years 9 months ago
Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery
Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in...
Tomer Faktor, Yonina C. Eldar, Michael Elad
ICASSP
2011
IEEE
14 years 1 months ago
An unsupervised algorithm for hybrid/morphological signal decomposition
The main contribution presented here is an adaptive/unsupervised iterative thresholding algorithm for sparse representation of signals which can be modeled as the sum of two compo...
Matthieu Kowalski, Thomas Rodet
ICASSP
2010
IEEE
14 years 9 months ago
Learning sparse systems at sub-Nyquist rates: A frequency-domain approach
We propose a novel algorithm for sparse system identification in the frequency domain. Key to our result is the observation that the Fourier transform of the sparse impulse respo...
Martin McCormick, Yue M. Lu, Martin Vetterli
ICASSP
2010
IEEE
14 years 9 months ago
Reconstruction of sparse signals from distorted randomized measurements
In this paper we show that, surprisingly, it is possible to recover sparse signals from nonlinearly distorted measurements, even if the nonlinearity is unknown. Assuming just that...
Petros Boufounos
ICASSP
2011
IEEE
14 years 1 months ago
Covariate-dependent dictionary learning and sparse coding
A dependent hierarchical beta process (dHBP) is developed as a prior for data that may be represented in terms of a sparse set of latent features (dictionary elements), with covar...
Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro, Dav...