2017
DOI: 10.1017/s1431927617001519
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Less is More: Bigger Data from Compressive Measurements

Abstract: Compressive sensing approaches are beginning to take hold in (scanning) transmission electron microscopy (S/TEM) [1,2,3]. Compressive sensing is a mathematical theory about acquiring signals in a compressed form (measurements) and the probability of recovering the original signal by solving an inverse problem [4]. The inverse problem is underdetermined (more unknowns than measurements), so it is not obvious that recovery is possible. Compression is achieved by taking inner products of the signal with measureme… Show more

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Cited by 2 publications
(1 citation statement)
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“…In order to reduce the computational complexity in the reconstruction stage, convolutional neural networks (CNNs) are applied to replace the optimization process. CNN-based methods [11,12,13,14,15] use big data [16] to train the networks that speed up the reconstruction stage. Mousavi, Patel, and Baraniuk [11] firstly propose deep learning approach to solve the CS recovery problem.…”
Section: Introductionmentioning
confidence: 99%
“…In order to reduce the computational complexity in the reconstruction stage, convolutional neural networks (CNNs) are applied to replace the optimization process. CNN-based methods [11,12,13,14,15] use big data [16] to train the networks that speed up the reconstruction stage. Mousavi, Patel, and Baraniuk [11] firstly propose deep learning approach to solve the CS recovery problem.…”
Section: Introductionmentioning
confidence: 99%