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Compressed sensing

Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal by finding solutions to underdetermined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it…

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Compressed sensing

Nodes83
Edges82
Triples89
Avg. degree1.98
Density0.024096
Components1

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Compressed sensing

Top relations

related to history · 18
Compressed sensing → At, Brown, Compressed, Dantzig's, Following, George, Huber, In, It, Laplace, LASSO, Nyquist, Peter, Robert Tibshirani, Shannon, Sparse, The, This
related to Further reading · 17
Compressed sensing → AMS What's Happening, Compressed Sensing Makes Every, Compressive Sensing, Georgia Tech, Hi-Res Samples Wired Magazine, IEEE Signal Processing Society, Mark Davenport, Mathematical Sciences, Part, Pixel Count, Rice University, Sensing Resources, SigView, The Fundamentals, Turn Lo-Res Datasets Into, Tutorial Library, Using Math
related to Solution / reconstruction method · 10
Compressed sensing → Compressed, David Donoho, Emmanuel Candès, However, In, Justin Romberg, Terence Tao, The, Therefore, This
related to Photography · 5
Compressed sensing → Bell Labs, Compressed, Image, Rice University, The
related to Underdetermined linear system · 5
Compressed sensing → An, However, In, Not, The
related to Network tomography · 4
Compressed sensing → Compressed, Internet, Moreover, Network
related to Aperture synthesis astronomy · 3
Compressed sensing → Fourier, In, The Högbom CLEAN
related to Magnetic resonance imaging · 3
Compressed sensing → Compressed, ISTAFISTASISTAePRESSEWISTAEWISTARS, Reconstruction
related to Speech processing · 3
Compressed sensing → CS, In, Sparse
related to Holography · 2
Compressed sensing → Compressed, It

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Important terminology

displaystyle signal sensing image sparse used compressed method reconstruction sampling one field orientation linear noise iterative gradient total cs sparsity

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
edgesinstance ofwhile retaining important information0.80text
would reduce the total variation of the signalinstance ofwhile retaining important information0.80text
make the signal subject closer to the original signal in the problem.For the purpose of signalinstance ofwhile retaining important information0.80text
image reconstructioninstance ofwhile retaining important information0.80text
ℓ 1instance ofwhile retaining important information0.80text
streaking.Iterative model using a directional orientation fieldinstance ofIt also effectively suppresses and removes any form of image noise and image artifacts0.80text
directional total variationTo prevent over-smoothing of edgesinstance ofIt also effectively suppresses and removes any form of image noise and image artifacts0.80text
texture detailsinstance ofIt also effectively suppresses and removes any form of image noise and image artifacts0.80text
to obtain a reconstructed CS image which is accurateinstance ofIt also effectively suppresses and removes any form of image noise and image artifacts0.80text
robust to noiseinstance ofIt also effectively suppresses and removes any form of image noise and image artifacts0.80text
artifactsinstance ofIt also effectively suppresses and removes any form of image noise and image artifacts0.80text
this method is usedinstance ofIt also effectively suppresses and removes any form of image noise and image artifacts0.80text

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