In digital image different kinds of noises exist in an image and a variety of noise reduction techniques are available to perform de-noising. Selection of the de-noising algorithm depends on the types of noise. Gaussian noise, speckle noise, salt & pepper noise, shot noise are types of noises that are present in an image. The principle approach of image de-noising is filtering. Available filter...

In this paper, the frequency offset estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems are addressed. First, a maximum-likelihood (ML) estimation scheme in non-Gaussian noise is proposed, and then a simpler estimation scheme based on the ML estimation scheme is presented. Numerical results show that the proposed schemes offer robust...

Starting with theWigner distribution formulation for beam wave propagation in Hölder continuous non-Gaussian random refractive index fields we show that the wave beam regime naturally leads to the white-noise scaling limit and converges to a Gaussian white-noise model which is characterized by the martingale problem associated to a stochastic differential-integral equation of the Itô type. In t...

MOTIVATION Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus...

Ultrasound imaging is widely used in the field of medicine. It is used for imaging soft tissues in organs like liver, kidney, spleen, uterus, heart, brain etc. The common problem in ultrasound image is speckle noise which is caused by the imaging technique used, that may be based on coherent waves such as acoustic to laser imaging. The denoising is to be performed to improve the image quality f...

Most studies concerned with the effects of noise on evolutionary computation have assumed a Gaussian noise model. However, practical optimization strategies frequently face situations where the noise is not Gaussian, and sometimes it does not even have a finite variance. In particular, outliers may be present. In this paper, Cauchy distributed noise is used for modeling such situations. A perfo...

In this paper, we study the effects of different ASE noise models on the performance of turbo code (TC) decoders. A soft-decoding algorithm, the Bahl, Cocke, Jelinek, and Raviv (BCJR) decoding algorithm [1], is generally used in the TC decoders. The BCJR algorithm is a maximum a posteriori probability (MAP) algorithm, and is very sensitive to the noise statistics. The Gaussian approximation of ...

In this paper, we model the noise present in middle and upper layers of the atmosphere for the data collected from the Indian MST Radar. People carry out their analysis assuming that the noise is Gaussian and in fact, in most of the scenarios, the noise is Gaussian. There is a much chance of getting inaccurate results if it is not. Gaussianity tests namely Autocorrelation (AC) and Power Spectra...

In this paper, we present a capacity analysis for broadband power line communication (PLC) channels, impaired by the Bernoulli–Gaussian impulsive noise, exploiting both orthogonal frequency division multiplexing (OFDM) and singlecarrier frequency-domain equalisation (SC-FDE) techniques. First, we investigate point-to-point communications and formulate the continuous signal model for OFDM and SC...

This paper establishes the global asymptotic equivalence between a Poisson process with variable intensity and white noise with drift under sharp smoothness conditions on the unknown function. This equivalence is also extended to density estimation models by Poissonization. The asymptotic equivalences are established by constructing explicit equivalence mappings. The impact of such asymptotic e...