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ICASSP
2011
IEEE
12 years 11 months ago
Dictionary learning of convolved signals
Assuming that a set of source signals is sparsely representable in a given dictionary, we show how their sparse recovery fails whenever we can only measure a convolved observation...
Daniele Barchiesi, Mark D. Plumbley
ICASSP
2008
IEEE
14 years 1 months ago
Blind deconvolution for sparse molecular imaging
This paper considers the image reconstruction problem when the original image is assumed to be sparse and when limited information of the point spread function (PSF) is available....
Kyle Herrity, Raviv Raich, Alfred O. Hero
SIAMSC
2010
215views more  SIAMSC 2010»
13 years 5 months ago
A Fast Algorithm for Sparse Reconstruction Based on Shrinkage, Subspace Optimization, and Continuation
We propose a fast algorithm for solving the ℓ1-regularized minimization problem minx∈Rn µ x 1 + Ax − b 2 2 for recovering sparse solutions to an undetermined system of linea...
Zaiwen Wen, Wotao Yin, Donald Goldfarb, Yin Zhang
MMAS
2010
Springer
13 years 2 months ago
A Nonlinear PDE-Based Method for Sparse Deconvolution
In this paper, we introduce a new nonlinear evolution partial differential equation for sparse deconvolution problems. The proposed PDE has the form of continuity equation that ar...
Yu Mao, Bin Dong, Stanley Osher
VALUETOOLS
2006
ACM
176views Hardware» more  VALUETOOLS 2006»
14 years 1 months ago
How to solve large scale deterministic games with mean payoff by policy iteration
Min-max functions are dynamic programming operators of zero-sum deterministic games with finite state and action spaces. The problem of computing the linear growth rate of the or...
Vishesh Dhingra, Stephane Gaubert