Compressed Sensing

By Gitta Kutyniok. 2014

4,106 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Electrical and Electronic Engineering

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Citations in the corpus

Learning stable and predictive structures in kinetic systems (2018, background): “Introduction Quantitative models of kinetic systems have become a cornerstone of the modern natural sciences and are universally used in scientific fields as diverse as physics, neuroscience, genetics, bioprocessing, robotics or economics Friston et al. [2003], Chen et al.”
The landscape of empirical risk for nonconvex losses (2016, background): “ is also known as ‘Kolmogorov asymptotics’ [Ser13]. 3 parameter vector θ0 is s0-sparse. This setting is relevant to a large number of applications, ranging from genomics [PZB+10] to signal processing [Don06]. In order to promote sparse estimates, we study the following ℓ1-regularized non-convex prob”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, methodology): “s-invariant feature mapping by leveraging the weight vectors of the binary classiﬁers learned in the source and target domains. They show that this learning task can be casted as a Compressed Sensing [66] problem and they use the ECOC scheme to generate a sufﬁcient number of binary classiﬁers given ”
Low-Dimensional Approach for Reconstruction of Airfoil Data via Compressive Sensing (2015, methodology): “It has been shown in [1,2,20] that the sparse signal can be reliably reconstructed based on a small number of such random projections via optimization techniques (such as l1 norm minimization).”
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, methodology): “Since the introduction of compressed sensing [16, 17] and its adoption for MRI [18–20], nonlinear regularization terms, in particular `1-norm based ones, are popular in image reconstruction and are commonly used in parallel imaging [19, 21– 26].”
A SIGNIFICANCE TEST FOR THE LASSO (2013, background): “9), Friedman et al. (2010), Becker, Bobin & Candes (2011), Boyd et al. (2011), Becker, Candes & Grant (2011); and for theoretical work see, e.g., Greenshtein & Ritov (2004), Fuchs (2005), Donoho (2006), Candes & Tao (2006), Zhao & Yu (2006), Wainwright (2009), Candes & Plan (”
Going deeper into action recognition: A survey (2016, background): “2 AGGREGATION WITH SPATIOTEMPORAL DICTIONARY LEARNING AND SPARSE CODING Sparse coding has become a popular choice in neuroscience, information theory, signal processing, and other related areas (Olshausen and Field, 1997; Candès et al., 2006; Donoho, 2006; Elad, 2010) in the last decade.”
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction (2017, methodology): “quisition process. This is because data samples of an MR image are acquired sequentially in kspace and the speed at which k-space can be traversed is limited by physiological and hardware constraints [1]. A long data acquisition procedure imposes signiﬁcant demands on patients, making this imaging m”
Compressive sensing: From theory to applications, a survey (2013, methodology): “As per available literature, Φ can be a Gaussian [18], Bernoulli [19], Fourier, or incoherent measurement matrix [12].”