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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
Embedding prior knowledge within compressed sensing by neural networks
IEEE Transactions on Neural Networks, Volume 22, No. 10, Article 6009227, Year 2011
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Description
In the compressed sensing framework, different algorithms have been proposed for sparse signal recovery from an incomplete set of linear measurements. The most known can be classified into two categories: ℓ1 norm minimization-based algorithms and ℓ0 pseudo-norm minimization with greedy matching pursuit algorithms. In this paper, we propose a modified matching pursuit algorithm based on the orthogonal matching pursuit (OMP). The idea is to replace the correlation step of the OMP, with a neural network. Simulation results show that in the case of random sparse signal reconstruction, the proposed method performs as well as the OMP. Complexity overhead, for training and then integrating the network in the sparse signal recovery is thus not justified in this case. However, if the signal has an added structure, it is learned and incorporated in the proposed new OMP. We consider three structures: first, the sparse signal is positive, second the positions of the non zero coefficients of the sparse signal follow a certain spatial probability density function, the third case is a combination of both. Simulation results show that, for these signals of interest, the probability of exact recovery with our modified OMP increases significantly. Comparisons with ℓ1 based reconstructions are also performed. We thus present a framework to reconstruct sparse signals with added structure by embedding, through neural network training, additional knowledge to the decoding process in order to have better performance in the recovery of sparse signals of interest. © 2011 IEEE.
Authors & Co-Authors
Merhej, Dany
France, Lyon
Université de Lyon
France, Villeurbanne
Creatis Centre de Recherche en Acquisition et Traitement de L'image Pour la Santé
Lebanon, Beirut
Université Libanaise
Diab, Chaouki
Lebanon
Institute of Applied and Economic Sciences
Khalil, M. Ali
Lebanon, Beirut
Université Libanaise
Prost, Rémy
France, Villeurbanne
Creatis Centre de Recherche en Acquisition et Traitement de L'image Pour la Santé
Statistics
Citations: 22
Authors: 4
Affiliations: 4
Identifiers
Doi:
10.1109/TNN.2011.2164810
ISSN:
10459227
Study Design
Case-Control Study