Results 11 to 20 of about 152,526 (313)
Universal Compressed Sensing [PDF]
2016 IEEE International Symposium on Information Theory (ISIT), 2016 In this paper, the problem of developing universal algorithms for compressed sensing of stochastic processes is studied. First, R\'enyi's notion of information dimension (ID) is generalized to analog stationary processes.
Jalali, Shirin, Poor, H. Vincent
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From compression to compressed sensing [PDF]
2013 IEEE International Symposium on Information Theory, 2013 Can compression algorithms be employed for recovering signals from their underdetermined set of linear measurements? Addressing this question is the first step towards applying compression algorithms for compressed sensing (CS).
Jalali, Shirin, Maleki, Arian
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"Compressed" Compressed Sensing
2010 IEEE International Symposium on Information Theory, 2010 The field of compressed sensing has shown that a sparse but otherwise arbitrary vector can be recovered exactly from a small number of randomly constructed linear projections (or samples).
Gastpar, Michael, Reeves, Galen
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Applied and Computational Harmonic Analysis, 2023 We introduce the broad subclass of algebraic compressed sensing problems, where structured signals are modeled either explicitly or implicitly via polynomials. This includes, for instance, low-rank matrix and tensor recovery. We employ powerful techniques from algebraic geometry to study well-posedness of sufficiently general compressed sensing ...
Breiding, Paul +3 more
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Compression-Based Compressed Sensing [PDF]
IEEE Transactions on Information Theory, 2017 Modern compression algorithms exploit complex structures that are present in signals to describe them very efficiently. On the other hand, the field of compressed sensing is built upon the observation that "structured" signals can be recovered from their under-determined set of linear projections.
Farideh E. Rezagah +3 more
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Compressed Sensing in Astronomy [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2008 Recent advances in signal processing have focused on the use of sparse representations in various applications. A new field of interest based on sparsity has recently emerged: compressed sensing. This theory is a new sampling framework that provides an alternative to the well-known Shannon sampling theory.
Jérôme Bobin +2 more
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Kinetic Compressive Sensing [PDF]
2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2017 5 pages, 6 figures, Submitted to the Conference Record of "IEEE Nuclear Science Symposium and Medical Imaging Conference (IEEE NSS-MIC) 2017"
Scipioni Michele +6 more
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Quantization and Compressive Sensing [PDF]
, 2015 35 pages, 20 figures, to appear in Springer book "Compressed Sensing and Its Applications ...
Boufounos, Petros +3 more
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Compressed wavefront sensing [PDF]
Optics Letters, 2014 We report on an algorithm for fast wavefront sensing that incorporates sparse representation for the first time in practice. The partial derivatives of optical wavefronts were sampled sparsely with a Shack-Hartman wavefront sensor (SHWFS) by randomly subsampling the original SHWFS data to as little as 5%.
Ryan P. McNabb +3 more
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Chinese Journal of Magnetic Resonance, 2018 Compressed sensing is widely used in accelerated magnetic resonance imaging (MRI) to reduce scan time. With compressed sensing, high-quality MR images could be acquired and reconstructed with only a small amount of K space data.
CHAI Qing-huan +2 more
doaj +1 more source