Results 11 to 20 of about 141,696 (278)
Adaptive Sensing for Sparse Signal Recovery [PDF]
2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009 The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recovered from a relatively small number of samples in the form of random projections. However, in severely resource-constrained settings even CS techniques may fail, and thus, a less aggressive goal of partial signal recovery is reasonable.
Haupt, J., Nowak, R., Castro, R.M.
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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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Multi-sparse signal recovery for compressive sensing [PDF]
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012 Signal recovery is one of the key techniques of Compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains.
Vos, Maarten +4 more
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Sparse Analysis Recovery via Iterative Cosupport Detection Estimation
IEEE Access, 2021 Cosparse analysis model (CAM) provides a new signal processing paradigm for recovering cosparse signals with respect to a given analysis operator from the undersampled linear measurements in the context of emerging theory of compressed sensing (CS).
Heping Song +3 more
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IET Signal Processing, 2023 Due to its self‐regularising nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications.
Zonglong Bai +3 more
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A Multi-Source Separation Approach Based on DOA Cue and DNN
Applied Sciences, 2022 Multiple sound source separation in a reverberant environment has become popular in recent years. To improve the quality of the separated signal in a reverberant environment, a separation method based on a DOA cue and a deep neural network (DNN) is ...
Yu Zhang +3 more
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Recovery of Sparsely Corrupted Signals [PDF]
IEEE Transactions on Information Theory, 2012 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 ...
Studer, Christoph +3 more
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Number of measurements in sparse signal recovery [PDF]
2009 IEEE International Symposium on Information Theory, 2009 6 pages, 1 figure.
Tune, Paul +2 more
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ISAR Imaging Algorithm Based on Iterative Weighted L2/L1 Norm Block Sparse Signal Recovery [PDF]
Jisuanji gongcheng, 2018 In order to realize fast and high resolution Inverse Synthetic Aperture Radar(ISAR)imaging,an iterative weighted L2/L1 norm block sparse recovery algorithm for ISAR imaging is proposed based on the target’s intrinsic block sparse structure information ...
FENG Junjie,ZHANG Gong
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Sparse signal reconstruction by swarm intelligence algorithms
Engineering Science and Technology, an International Journal, 2021 This study introduces a new technique for sparse signal reconstruction. In general, there are two classes of algorithms in the recovery of sparse signals: greedy approaches and l1-minimization methods. The proposed method employs swarm intelligence based
Murat Emre Erkoç, Nurhan Karaboğa
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