Results 1 to 10 of about 22,937 (263)
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 ...
Shirin Jalali, Arian Maleki
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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 Ebrahim Rezagah +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, Rényi's notion of information dimension (ID) is generalized to analog stationary processes. This provides a measure of complexity for such processes and is connected to the number of measurements required for their accurate ...
Shirin Jalali, H. Vincent Poor
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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%.
James, Polans +3 more
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"Compressed" Compressed Sensing
CoRR, 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). The question addressed in this paper is whether an even smaller number of samples is sufficient when there exists prior knowledge about the distribution of the ...
Galen Reeves, Michael Gastpar
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Hierarchical Compressed Sensing
, 2022 Compressed sensing is a paradigm within signal processing that provides the means for recovering structured signals from linear measurements in a highly efficient manner. Originally devised for the recovery of sparse signals, it has become clear that a similar methodology would also carry over to a wealth of other classes of structured signals. In this
Jens Eisert +4 more
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A remark on Compressed Sensing [PDF]
Mathematical Notes, 2007 A classical problem in signal processing is the recovery problem: One is interested in reconstructing a vector \(u \in \mathbb R^m\) from given linear functionals \((u, \phi_j)\), \(j = 1, 2, \ldots, n\), with some known values \(\phi_1, \ldots, \phi_n \in \mathbb R^m\). In most typical applications, \(n\) is substantially smaller than \(m\).
Kashin, B. S., Temlyakov, V. N.
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International Journal of Engineering Technologies and Management Research, 2020 Compressive sensing is a relatively new technique in the signal processing field which allows acquiring signals while taking few samples. It works on two principles: sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality.
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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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CoRR, 2019 Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and recover an image from only a few random measurements. CS is flexible and data efficient, but its application has been restricted by the strong assumption of sparsity and costly
Yan Wu 0010 +2 more
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