Results 31 to 40 of about 277,175 (273)
Sparse signal recovery from sparsely corrupted measurements [PDF]
2011 IEEE International Symposium on Information Theory Proceedings, 2011 We investigate the recovery of signals exhibiting a sparse representation in a general (i.e., possibly redundant or incomplete) dictionary that are corrupted by additive noise admitting a sparse representation in another general dictionary. This setup covers a wide range of applications, such as image inpainting, super-resolution, signal separation ...
Christoph Studer +3 more
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A Review of Sparse Recovery Algorithms
IEEE Access, 2019 Nowadays, a large amount of information has to be transmitted or processed. This implies high-power processing, large memory density, and increased energy consumption.
Elaine Crespo Marques +4 more
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Sparse polynomial interpolation: sparse recovery, super-resolution, or Prony? [PDF]
Advances in Computational Mathematics, 2019 We show that the sparse polynomial interpolation problem reduces to a discrete super-resolution problem on the $n$-dimensional torus. Therefore the semidefinite programming approach initiated by Cand s \\& Fernandez-Granda \cite{candes\_towards\_2014} in the univariate case can be applied.
Josz, Cédric +2 more
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Recovery of sparse urban greenhouse gas emissions [PDF]
Geoscientific Model Development, 2022 To localize and quantify greenhouse gas emissions from cities, gas concentrations are typically measured at a small number of sites and then linked to emission fluxes using atmospheric transport models. Solving this inverse problem is challenging because
B. Zanger +4 more
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Subspace Methods for Joint Sparse Recovery [PDF]
, 2011 We propose robust and efficient algorithms for the joint sparse recovery problem in compressed sensing, which simultaneously recover the supports of jointly sparse signals from their multiple measurement vectors obtained through a common sensing matrix ...
Bresler, Yoram +2 more
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Saliency Detection with Sparse Prototypes: An Approach Based on Multi-Dictionary Sparse Encoding
MATEC Web of Conferences, 2018 This paper proposes a bottom-up saliency detection algorithm based on multi-dictionary sparse recovery. Firstly, the SLIC algorithm is used to segment the image into superpixels in multilevel and atoms with a high background possibility are selected from
Wang Jun, Wu Zemin, Tian Chang, Hu Lei
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Nonconvex Penalized Regularization for Robust Sparse Recovery in the Presence of
IEEE Access, 2018 Nonconvex penalties have recently received considerable attention in sparse recovery based on Gaussian assumptions. However, many sparse recovery problems occur in the presence of impulsive noises. This paper is concerned with the analysis and comparison
Yunyi Li +5 more
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Deep Unfolded Gridless DOA Estimation Networks Based on Atomic Norm Minimization
Remote Sensing, 2022 Deep unfolded networks have recently been regarded as an essential way to direction of arrival (DOA) estimation due to the fast convergence speed and high interpretability. However, few consider gridless DOA estimation.
Hangui Zhu +4 more
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Block-Sparse Recovery via Convex Optimization [PDF]
, 2012 Given a dictionary that consists of multiple blocks and a signal that lives in the range space of only a few blocks, we study the problem of finding a block-sparse representation of the signal, i.e., a representation that uses the minimum number of ...
Ehsan Elhamifar +3 more
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