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Noise (signal processing)

About: Noise (signal processing) is a(n) research topic. Over the lifetime, 61013 publication(s) have been published within this topic receiving 621165 citation(s).

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Proceedings ArticleDOI: 10.1109/CVPR.2005.38
20 Jun 2005-
Abstract: We propose a new measure, the method noise, to evaluate and compare the performance of digital image denoising methods. We first compute and analyze this method noise for a wide class of denoising algorithms, namely the local smoothing filters. Second, we propose a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image. Finally, we present some experiments comparing the NL-means algorithm and the local smoothing filters.

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Topics: Non-local means (74%), Video denoising (66%), Smoothing (60%) ...read more

5,832 Citations


Journal ArticleDOI: 10.1109/29.32276
R. Roy1, Thomas Kailath1Institutions (1)
Abstract: An approach to the general problem of signal parameter estimation is described. The algorithm differs from its predecessor in that a total least-squares rather than a standard least-squares criterion is used. Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise. It exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as MEM, Capon's MLM, and MUSIC. >

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5,794 Citations


Journal ArticleDOI: 10.1142/S1793536909000047
Zhaohua Wu, Norden E. Huang1Institutions (1)
Abstract: A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...

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Topics: White noise (60%), Noise (signal processing) (57%), Filter (signal processing) (52%) ...read more

5,108 Citations


Journal ArticleDOI: 10.1109/JRPROC.1962.288235
J. Nagumo1, S. Arimoto1, S. Yoshizawa1Institutions (1)
01 Oct 1962-
Abstract: To electronically simulate an animal nerve axon, the authors made an active pulse transmission line using tunnel diodes. The equation of propagation for this line is the same as that for a simplified model of nerve membrane treated elsewhere. This line shapes the signal waveform during transmission, that is, there being a specific pulse-like waveform peculiar to this line, smaller signals are amplified, larger ones are attenuated, narrower ones are widened and those which are wider are shrunk, all approaching the above-mentioned specific waveform. In addition, this line has a certain threshold value in respect to the signal height, and signals smaller than the threshold or noise are eliminated in the course of transmission. Because of the above-mentioned shaping action and the existence of a threshold, this line makes possible highly reliable pulse transmission, and will be useful for various kinds of information-processing systems.

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Topics: Waveform (56%), Transmission (telecommunications) (55%), Pulse shaping (55%) ...read more

3,140 Citations


Journal ArticleDOI: 10.1089/BRAIN.2012.0073
06 Aug 2012-Brain connectivity
Abstract: Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn (www.nitrc.org/projects/conn) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method all...

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2,433 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202238
20211,674
20202,196
20192,610
20182,361
20172,326

Top Attributes

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Topic's top 5 most impactful authors

François Chapeau-Blondeau

32 papers, 911 citations

Seiichi Nakamori

25 papers, 150 citations

Jacob Benesty

18 papers, 307 citations

Volodymyr Ponomaryov

17 papers, 188 citations

Hiroshi Saruwatari

17 papers, 108 citations

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