Results 21 to 30 of about 14,124 (260)
Deterministic Sensing Matrices in Compressive Sensing: A Survey
The Scientific World Journal, 2013 Compressive sensing is a sampling method which provides a new approach to efficient signal compression and recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements.
Thu L. N. Nguyen, Yoan Shin
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Jurnal Elkomika, 2020 ABSTRAK Watermarking pada citra medis dilakukan untuk melindungi hak kepemilikan dan keaslian sebuah citra medis. Proses embedding dan extraction dirancang menggunakan metode Stationary Wavelet Transform (SWT) dan Statistical Mean Manipulation (SMM ...
YASQI HAFIZHANA +3 more
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SoftwareX, 2018 Compressive sensing is a computational framework for acquisition and processing of sparse signals at sampling rates below the rates mandated by the Nyquist sampling theorem.
Ervin Sejdić +2 more
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International Journal of Engineering Technologies and Management Research, 2020 Compressive sensing is a relatively new technique in the signal processing field which allows acquiring signals while taking few samples. It works on two principles: sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality.
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Sensors, 2014 Compressive Sensing Imaging (CSI) is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Since the characteristics of Compressive Sensing (CS) acquisition are very different from traditional image
Xiangwei Li +4 more
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A New Method for EEG Compressive Sensing
Advances in Electrical and Computer Engineering, 2012 The paper investigates the possibility of using compressive sensing techniques for the acquisition and reconstruction of EEG signals containing the evoked potential P300. A method of EEG compressive sensing based on the physiological correlation of EEG
FIRA, M., GORAS, L.
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A remark on Compressed Sensing [PDF]
Mathematical Notes, 2007 A classical problem in signal processing is the recovery problem: One is interested in reconstructing a vector \(u \in \mathbb R^m\) from given linear functionals \((u, \phi_j)\), \(j = 1, 2, \ldots, n\), with some known values \(\phi_1, \ldots, \phi_n \in \mathbb R^m\). In most typical applications, \(n\) is substantially smaller than \(m\).
Kashin, B. S., Temlyakov, V. N.
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Adaptive compressive sensing of images using error between blocks
International Journal of Distributed Sensor Networks, 2018 Block compressive sensing of image results in blocking artifacts and blurs when reconstructing images. To solve this problem, we propose an adaptive block compressive sensing framework using error between blocks.
Ran Li, Xiaomeng Duan, Yongfeng Lv
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Perceptual Compressive Sensing [PDF]
, 2018 Accepted by The First Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2018). This is a pre-print version (not final version)
Jiang Du 0011 +3 more
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CoRR, 2019 Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and recover an image from only a few random measurements. CS is flexible and data efficient, but its application has been restricted by the strong assumption of sparsity and costly
Yan Wu 0010 +2 more
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