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Cosparsity in Compressed Sensing
2015Analysis l1-recovery is a strategy of acquiring a signal, that is sparse in some transform domain, from incomplete observations. In this chapter we give an overview of the analysis sparsity model and present theoretical conditions that guarantee successful nonuniform and uniform recovery of signals from noisy measurements.
Maryia Kabanava, Holger Rauhut
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Compressed sensing of compressible signals
2017 IEEE International Symposium on Information Theory (ISIT), 2017A novel low-complexity robust-to-noise iterative algorithm named compression-based gradient descent (C-GD) algorithm is proposed. C-GD is a generic compressed sensing recovery algorithm, that at its core, employs compression codes, such as JPEG2000 and MPEG4.
Sajjad Beygi +3 more
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2017
We consider the problem of sparse signal recovery in dynamic sensing scenarios. Specifically, we study the recovery of a sparse time-varying signal from linear measurements of a single static sensor that are taken at two different points in time. This setup can be modelled as observing a single signal using two different sensors – a real one and a ...
Dalitz, Robert +2 more
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We consider the problem of sparse signal recovery in dynamic sensing scenarios. Specifically, we study the recovery of a sparse time-varying signal from linear measurements of a single static sensor that are taken at two different points in time. This setup can be modelled as observing a single signal using two different sensors – a real one and a ...
Dalitz, Robert +2 more
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Accelerating SENSE using compressed sensing
Magnetic Resonance in Medicine, 2009AbstractBoth parallel MRI and compressed sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data. The combination of parallel MRI and CS for further acceleration is of great interest. In this paper, we propose a novel method to combine sensitivity encoding (SENSE), one of the standard methods for ...
Bo Liu +4 more
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Proceedings of the fifth international conference on Information processing in sensor networks - IPSN '06, 2006
Compressive sampling is an emerging theory that is based on the fact that a relatively small number of random projections of a signal can contain most of its salient information. In this paper, we introduce the concept of compressive wireless sensing for sensor networks in which a fusion center retrieves signal field information from an ensemble of ...
Waheed U. Bajwa +3 more
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Compressive sampling is an emerging theory that is based on the fact that a relatively small number of random projections of a signal can contain most of its salient information. In this paper, we introduce the concept of compressive wireless sensing for sensor networks in which a fusion center retrieves signal field information from an ensemble of ...
Waheed U. Bajwa +3 more
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Compressive Sensing and Compressive Holography
2015It is well known from communication theory that for a sampled signal, the sampling rate must be greater than twice the signal bandwidth for faithful reproduction of the original signal. The concept of sampling at the Nyquist rate was postulated by Shannon in 1949; in the same year, Golay introduced the idea of artificial discrete multiplex coding in ...
Rola Aylo +2 more
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Compressive Sensing in Acoustic Imaging
2015Acoustic sensing is at the heart of many applications, ranging from underwater sonar and nondestructive testing to the analysis of noise and their sources, medical imaging, and musical recording. This chapter discusses a palette of acoustic imaging scenarios where sparse regularization can be leveraged to design compressive acoustic imaging techniques.
Bertin, Nancy +3 more
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Rethinking Compressive Sensing
2018 26th European Signal Processing Conference (EUSIPCO), 2018In this paper we show that Compressive Sensing (CS) can be casted as an impulse response estimation problem. Using this interpretation we re-obtain some theoretical results of CS in a simple manner. Moreover, we prove that in the case of a randomly generated sensing matrix, reconstruction probability depends on the kurtosis of the distribution used for
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Sensing Matrices in Compressed Sensing
2019One of the most important aspects of compressed sensing (CS) theory is an efficient design of sensing matrices. These sensing matrices are accountable for the required signal compression at the encoder end and its exact or approximate reconstruction at the decoder end.
Sanjay L. Nalbalwar, Yuvraj V. Parkale
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Compressed sensing for astrophysical signals
2016 IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2016In order to reduce power consumption and limit the amount of data acquired and stored for astrophysical signals, an emerging sampling paradigm called compressed sensing (also known as compressive sensing, compressive sampling, CS) could potentially be an efficient solution. The design of radio receiver architecture based on CS requires knowledge of the
Gargouri, Yosra +4 more
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