Results 21 to 30 of about 66,822 (262)
Sparse Signal Recovery via Exponential Metric Approximation
Tsinghua Science and Technology, 2017 Sparse signal recovery problems are common in parameter estimation, image processing, pattern recognition, and so on. The problem of recovering a sparse signal representation from a signal dictionary might be classified as a linear constraint ℓ0 ...
Jian Pan, Jun Tang, Wei Zhu
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Sparse recovery for discrete tomography [PDF]
2010 IEEE International Conference on Image Processing, 2010 Discrete tomography (DT) focuses on the reconstruction of a discrete valued image from few projection angles. Prior knowledge about the image can greatly increase the quality of the reconstructed image, especially when a small number of projections are available. In this paper, we show that DT can be formulated as a sparse signal recovery problem.
Yen-ting Lin +2 more
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On the Power of Adaptivity in Sparse Recovery [PDF]
2011 IEEE 52nd Annual Symposium on Foundations of Computer Science, 2011 The goal of (stable) sparse recovery is to recover a $k$-sparse approximation $x*$ of a vector $x$ from linear measurements of $x$. Specifically, the goal is to recover $x*$ such that ||x-x*||_p <= C min_{k-sparse x'} ||x-x'||_q for some constant $C$ and norm parameters $p$ and $q$.
Indyk, Piotr +2 more
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A Space-time Adaptive Processing Algorithm Based on Joint Sparse Recovery
Leida xuebao, 2014 Sparse recovery Space-Time Adaptive Processing (STAP) methods for obtaining the clutter spectrum require few training samples and can effectively suppress clutter in nonhomogeneous clutter environments.
Duan Ke-qing +4 more
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Block-Sparse Tensor Recovery [PDF]
IEEE Transactions on Information TheoryAccepted by IEEE Transactions on Information ...
Liyang Lu +4 more
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Sequential Testing for Sparse Recovery [PDF]
IEEE Transactions on Information Theory, 2014 This paper studies sequential methods for recovery of sparse signals in high dimensions. When compared to fixed sample size procedures, in the sparse setting, sequential methods can result in a large reduction in the number of samples needed for reliable signal support recovery.
Matthew Malloy, Robert D. Nowak
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An ALPS view of sparse recovery [PDF]
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011 We provide two compressive sensing (CS) recovery algorithms based on iterative hard-thresholding. The algorithms, collectively dubbed as algebraic pursuits (ALPS), exploit the restricted isometry properties of the CS measurement matrix within the algebra of Nesterov's optimal gradient methods.
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IEEE Access, 2023 Reconfigurable intelligent surfaces (RIS) are passive controllable arrays of small reflectors that direct electromagnetic energy towards or away from the target nodes, thereby allowing better management of signals and interference in a wireless network ...
Bharath Shamasundar +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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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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