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Fast Sparse Decomposition by Iterative Detection-Estimation
Arash Ali Amini, Massoud Babaie-Zadeh, Christian Jutten
Finding sparse solutions of underdetermined systems of linear equations is a fundamental problem in signal processing and statistics which has become a subject of interest in recen…
Bayesian Hypothesis Testing for Sparse Representation
Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten
In this paper, we propose a Bayesian Hypothesis Testing Algorithm (BHTA) for sparse representation. It uses the Bayesian framework to determine active atoms in sparse representatio…
On the stable recovery of the sparsest overcomplete representations in presence of noise
Massoud Babaie-Zadeh, Christian Jutten
Let x be a signal to be sparsely decomposed over a redundant dictionary A, i.e., a sparse coefficient vector s has to be found such that x=As. It is known that this problem is inhe…
On the Achievability of Cramér-Rao Bound In Noisy Compressed Sensing
Rad Niazadeh, Masoud Babaie-Zadeh, Christian Jutten
Recently, it has been proved in Babadi et al. that in noisy compressed sensing, a joint typical estimator can asymptotically achieve the Cramer-Rao lower bound of the problem.To pr…
Bayesian Cramér-Rao Bound for Noisy Non-Blind and Blind Compressed Sensing
Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten
In this paper, we address the theoretical limitations in reconstructing sparse signals (in a known complete basis) using compressed sensing framework. We also divide the CS to non-…
Sparse Recovery using Smoothed (SL0): Convergence Analysis
Hosein Mohimani, Massoud Babaie-Zadeh, Irina Gorodnitsky +1
Finding the sparse solution of an underdetermined system of linear equations has many applications, especially, it is used in Compressed Sensing (CS), Sparse Component Analysis (SC…