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Noise Removal Algorithm for Images Corrupted by Additive Gaussian Noise
"... ABSTRACTThis paper presents noise removal algorithm for gray scale images corrupted by additive Gaussian noise. A robust open close sequence filter based on mathematical morphology for high probability additive Gaussian noise removal is discussed. First, an additive Gaussian noise detector using ma ..."
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ABSTRACTThis paper presents noise removal algorithm for gray scale images corrupted by additive Gaussian noise. A robust open close sequence filter based on mathematical morphology for high probability additive Gaussian noise removal is discussed. First, an additive Gaussian noise detector using
On the Accuracy of the High SNR Approximation of the Differential Entropy of Signals in Additive Gaussian Noise
"... Abstract—One approach for analyzing the high signaltonoise ratio (SNR) capacity of noncoherent wireless communication systems is to ignore the noise component of the received signal in the computation of its differential entropy. In this paper we consider the error incurred by this approximation ..."
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Cited by 2 (1 self)
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by this approximation when the transmitter and the receiver have one antenna each, and the noise has a Gaussian distribution. For a general instance of this case, we show that the approximation error decays as 1/SNR. In addition, we consider the special instance in which the received signal corresponds to a signal
ZivZakaı̈ bound for harmonic retrieval in multiplicative and additive Gaussian noise
 in Proc. of IEEE Workshop on Statistical Signal Processing (SSP
, 2005
"... The paper addresses the problem of the lower performance bound evaluation for harmonic retrieval in multiplicative and additive noise, especially when the SNR is low and/or when the number of available samples is small. As a novelty, we express an accurate approximation of the ZivZakai bound in cl ..."
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Cited by 5 (0 self)
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The paper addresses the problem of the lower performance bound evaluation for harmonic retrieval in multiplicative and additive noise, especially when the SNR is low and/or when the number of available samples is small. As a novelty, we express an accurate approximation of the ZivZakai bound
The Estimation of the Frequency of a Complex Exponential in Additive Gaussian Noise
"... Abstract A new algorithm for the precise estimation of the frequency of a complex exponential signal in additive, complex, white Gaussian noise is presented. The algorithm has low computational complexity and is well suited for numerous real time applications. The DFT based algorithm performs an i ..."
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Abstract A new algorithm for the precise estimation of the frequency of a complex exponential signal in additive, complex, white Gaussian noise is presented. The algorithm has low computational complexity and is well suited for numerous real time applications. The DFT based algorithm performs
ROBUST SUPEREXPONENTIAL METHODS FOR BLIND EQUALIZATION OF AN SISOIIR SYSTEM WITH ADDITIVE GAUSSIAN NOISE
"... The present paper deals with the blind equalization problem of a singleinput singleoutput infinite impulse response (SISOIIR) system with additive Gaussian noise. To solve the problem, we propose a ”superexponential method” (SEM). The novel point of the proposed SEM is that even when Gaussian n ..."
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The present paper deals with the blind equalization problem of a singleinput singleoutput infinite impulse response (SISOIIR) system with additive Gaussian noise. To solve the problem, we propose a ”superexponential method” (SEM). The novel point of the proposed SEM is that even when Gaussian
Capacity of multiantenna Gaussian channels
 EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS
, 1999
"... We investigate the use of multiple transmitting and/or receiving antennas for single user communications over the additive Gaussian channel with and without fading. We derive formulas for the capacities and error exponents of such channels, and describe computational procedures to evaluate such form ..."
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Cited by 2923 (6 self)
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We investigate the use of multiple transmitting and/or receiving antennas for single user communications over the additive Gaussian channel with and without fading. We derive formulas for the capacities and error exponents of such channels, and describe computational procedures to evaluate
Image denoising using a scale mixture of Gaussians in the wavelet domain
 IEEE TRANS IMAGE PROCESSING
, 2003
"... We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vecto ..."
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Cited by 513 (17 self)
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coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable. We demonstrate through simulations with images contaminated by additive white Gaussian noise that the performance of this method substantially surpasses that of previously
Calibrating noise to sensitivity in private data analysis
 In Proceedings of the 3rd Theory of Cryptography Conference
, 2006
"... Abstract. We continue a line of research initiated in [10, 11] on privacypreserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the socalled true answer is the result of applying f to the datab ..."
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Cited by 649 (60 self)
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to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f =P i g(xi), where xi denotes
EURASIP Journal on Applied Signal Processing 2005:15, 2500–2513 c ○ 2005 Hindawi Publishing Corporation Restoration of Astrophysical Images—The Case of Poisson Data with Additive Gaussian Noise
, 2004
"... We consider the problem of restoring astronomical images acquired with charge coupled device cameras. The astronomical object is first blurred by the point spread function of the instrumentatmosphere set. The resulting convolved image is corrupted by a Poissonian noise due to low light intensity, t ..."
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, then, a Gaussian white noise is added during the electronic readout operation. We show first that the split gradient method (SGM) previously proposed can be used to obtain maximum likelihood (ML) iterative algorithms adapted in such noise combinations. However, when ML algorithms are used for image
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
, 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
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Cited by 483 (2 self)
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that has been contaminated with additive noise, the goal is to identify which elementary signals participated and to approximate their coefficients. Although many algorithms have been proposed, there is little theory which guarantees that these algorithms can accurately and efficiently solve the problem
Results 1  10
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