We have devised a way of segmentation and progressive transmission of MRI images. Entropy maximization using Particle Swarm Algorithm (PSO) is used to get the Region of Interest (ROI). The ROI is de-noised using Multi-Wavelet Analysis. Soft Thresholding together with Stationary Wavelet is used for de-noising purpose. Varying percentages of Discrete Cosine Transform Coefficients are used for the...

he image processing is the technology through which information of image get processed . the noises are the extra pixels which get added on the image to reduce image quality. The authors proposed various techniques which works to remove noisy pixels from the image. In this paper, various image de-nosing techniques has been reviewed and discuss in terms of their outcomes. KeywordsNoise, De-noisi...

This article presents a new scheme for movie de-scratching, de-noising and de-blotching based on wave atom transform and nonparametric model. According to the analysis of the noise, scratches and blotches in the film, we establish a hybrid model for movie signal. In the proposed model, we model scratch as directional additive noise, which can be represented effectively by a new multi-scale anal...

We construct an estimation and de-noising procedure for an input signal perturbed by a continuous-time Gaussian noise, using the local and occupation times of Gaussian processes. The method relies on the almost-sure minimization of a Stein Unbiased Risk Estimator (SURE) obtained through integration by parts on Gaussian space, and applied to shrinkage estimators which are constructed by soft and...

Using integration by parts on Gaussian space we construct a Stein Unbiased Risk Estimator (SURE) for the drift of Gaussian processes, based on their local and occupation times. By almost-sure minimization of the SURE risk of shrinkage estimators we derive an estimation and de-noising procedure for an input signal perturbed by a continuous-time Gaussian noise.

The paper is devoted to time series prediction using linear, perceptron and Elman neural networks of the proposed pattern structure. Signal wavelet de-noising in the initial stage is discussed as well. The main part of the paper is devoted to the comparison of different models of time series prediction. The proposed algorithm is applied to the real signal representing gas consumption.

In this paper the noise suppression with the help of a wavelet transform in synthesis imaging is presented. The method has been used to treat dirty maps observed with the Miyun Synthesis Radio Telescope (MSRT), and the results indicate that the de-noising with the help of the wavelet transform is satisfactory and prospective.

In this paper we provide a theoretical framework of de-noising for UMTS TDD-like mobile radio communication systems. Based on the Bayesian approach, we show how to denoise channel estimates provided by the Training-based estimation procedure. The proposed schemes allow not only for eliminating major drawbacks of hard thresholding but also for a low complexity implementation.

This paper proposes a novel efficient multistage algorithm to extract source speech signals from noisy convolutive mixture. The proposed approach comprises two stages named Blind Source Separation (BSS) and de-noising. A hybrid prior model separates the reverberant mixture in BSS stage. Moreover, we low- high-energy components by generalized multivariate Gaussian super-Gaussian models, respecti...

Wavelets are explored as a data smoothing (or de-noising) option for solution monitoring data in nuclear safeguards. In wavelet-smoothed data, the Gibbs phenomenon can obscure important data features that may be of interest. This paper compares wavelet smoothing to piecewise linear smoothing and local kernel smoothing, and illustrates that the Haar wavelet basis is effective for reducing the Gi...