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Compression-Based Compressed Sensing [PDF]
IEEE Transactions on Information Theory, 2016 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
Erkip, Elza +3 more
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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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Sequential Compressed Sensing [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2009 Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of random measurements. Existing results in compressed sensing literature have focused on characterizing the achievable performance ...
Malioutov, Dmitry +2 more
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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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Algebraic compressed sensing [PDF]
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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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). In this paper, we consider a family of compression algorithms $\mathcal{C}_r$, parametrized by rate $r$, for a compact class ...
Arian Maleki, Shirin Jalali
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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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Compressed sensing of twisted photons [PDF]
Optics Express, 2019 The ability to completely characterize the state of a quantum system is an essential element for the emerging quantum technologies. Here, we present a compressed-sensing inspired method to ascertain any rank-deficient qudit state, which we experimentally encode in photonic orbital angular momentum.
Frédéric Bouchard +10 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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