ABSTRACT-This 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

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

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 nu-merous real time applications. The DFT based algorithm performs

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 nov-elty, we express an accurate approximation of the Ziv-Zakai bound

The present paper deals with the blind equalization prob-lem of a single-input single-output infinite impulse response (SISO-IIR) system with additive Gaussian noise. To solve the problem, we propose a ”super-exponential method” (SEM). The novel point of the proposed SEM is that even when Gaussian

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

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

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

, then, a Gaussian white noise is added during the electronic read-out 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

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