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...

Wavelet transforms and other multi-scale analysis functions have been used for compact signal and image representations in de-noising, compression and feature detection processing problems for about twenty years. Numerous research works have proven that space-frequency and spacescale expansions with this family of analysis functions provided a very efficient framework for signal or image data. ...

Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classification algorithms. But it can also be considered as a natural generalization of linear principal component analysis. This gives rise to the question how to use nonlinear features for data compression, reconstruction, and de-noising, applications common in linear PCA. This is a nontrivial task, as...

In this research, we will work on the development of a new method for the removal of salt & pepper noise by creating a new hybridized filter using existing and/or new noise removal filters. The proposed filter will remove the noise with no or minimum image quality degradation. Salt & pepper noise degrades the quality of the image by hiding the details of objects in the image and also causes dam...

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.

Active infrared thermography is an attractive and highly reliable technique used for the non-destructive evaluation of test objects. In this paper, defect detection on subsurface STS304 metal specimen was performed by applying line-scanning method to induction thermography. general, camera are fixed in thermography, but can excite a uniform heat source because relative movement occurs. After th...

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.

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...