An Introduction To Compressive Sampling

By E.J. Candes, Michael B. Wakin. 2008

10,061 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics

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Citations in the corpus

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples (2017, background): “2006), as well as sparse recovery (Candès and Wakin 2008), L1-based adversarial examples have not been rigorously explored.”
Computational illumination for high-speed in vitro Fourier ptychographic microscopy (2015, background): “Reconstruction algorithms that explicitly take a priori information into account, such as sparsity based methods [29,30], could further improve capture speed; here, for generality, we choose not to make any assumptions on the sample.”
Learning interpretable classification rules using sequential rowsampling (2015, methodology): “Viewing these measurements as matrix multiplication by a bi nary sensing matrix in the Boolean algebra, where disjunction and conjunction replace linear algebraic addition and multiplication, es tablishes a close connection to traditional CS [16].”
Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing (2009, background): “whelming probability” [6]–[9] one may recover exactly if ; similar relationships hold when is compressible but not exactly sparse.”
Compressed data aggregation for energy efficient wireless sensor networks (2011, background): “As a newly developed signal processing technique, compressed sensing (CS) promises to deliver a full recovery of signals w.h.p. from far fewer measurements than their original dimension, as long as the signals are sparse or compressible in some domain [10].”
Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns (2017, background): “2) The number of measurements p must satisfy [99], [98], [33], [100], [104] p ∼ O ( K log( n/K )) .”
Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors (2012, background): “For a more theoretical and thorough background on compressed sensing, please refer to [9], [11], [15].”
Probabilistic reconstruction in compressed sensing: algorithms, phase diagrams, and threshold achieving matrices (2012, methodology): “In a second step, one uses computational power to reconstruct the original signal exactly [2, 3].”
Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach (2015, neutral): “, compressed sensing [49] and matrix completion [50].”
The engine behind (wall) turbulence: perspectives on scale interactions (2017, background): “In this sense, the statement of Candes & Wakin (2008) (from their tutorial article on compressive sensing) seems to hold: ‘Many natural signals are sparse or compressible in the sense that they have concise representations when expressed in the proper basis.’”