Embedding Prior Knowledge Within Compressed Sensing by Neural Networks

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: ℓ 1 norm minimization-based algorithms and ℓ 0 pseudo-norm minimization with greedy matching pu...

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Bibliographic Details
Published in:	IEEE transactions on neural networks Vol. 22; no. 10; pp. 1638 - 1649
Main Authors:	Merhej, D., Diab, C., Khalil, M., Prost, R.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.10.2011 Institute of Electrical and Electronics Engineers
Subjects:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial Intelligence Compressed Compressed sensing Computer science; control theory; systems Computer simulation Computer systems performance. Reliability Connectionism. Neural networks Correlation Data Compression - methods Decoding Exact sciences and technology Greedy algorithms Humans Indexes Matching Matching pursuit algorithms Models, Neurological Neural networks Neural Networks (Computer) Neurons orthogonal matching pursuit Recovery Signal processing Software Software Design Theoretical computing Training Random signal Probabilistic approach neural networks Minimization Decoding Case based reasoning Neural network orthogonal matching pursuit Inverse problem Restoration Greedy algorithm Probability density function Signal reconstruction Compressed sensing
ISSN:	1045-9227, 1941-0093, 1941-0093
Online Access:	Get full text
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