Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals
Low-dimensional signal structures naturally arise in a large set of applications in various fields such as medical imaging, machine learning, signal, and array processing. A ubiquitous low-dimensional structure in signals and images is sparsity, and a new sampling theory; namely, compressive sensing...
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Format: | Others |
Language: | en_US |
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Georgia Institute of Technology
2014
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Online Access: | http://hdl.handle.net/1853/50226 |