The aim of this paper is to investigate the effect of noises on performance of speech signal de-noising using the method based on wavelets, wiener filtering and ICA. Determination of voiced and unvoiced speech, low and high pitch, and methods for selecting appropriate wavelets for speech compression are discussed. Discrete wavelet transform (DWT) has been applied for suppression of additive noi...

This paper investigates the technique of wavelet threshold de-noising with Independent Component Analysis (ICA) for noisy image separation. In the first approach, noisy mixed images are separated using fast ICA algorithm and then wavelet thresholding is used to de-noise. The second approach uses wavelet threshold to de-noise and then use the fast ICA algorithm to separate the de-noised images. ...

A new proposal of blind channel estimation method for long term evoluation (LTE) based on combining advantages of denoising property of wavelet transform (WT) with blind estimation capability of independent component analysis (ICA) called wavelet denoising of ICA (WD-ICA) was presented. This new method increased the spectral efficiency compared to training based methods, and provided considerab...

This paper reports on numerical experiments on the ‘independent component analysis’ (ICA) of some nonGaussian stochastic processes. It is found that the orthonormal basis discovered by ICA are strikingly close to wavelet basis.

Synthetic aperture radar (SAR) imaging has the characteristics of acquiring remote sensing data under all weather and all time. So SAR image change detection techniques have large advantage in abruptly natural and man-made disaster. Inherent speckle noise of SAR image badly obstructs the applications for SAR image change detection. SAR image belongs to non-Gaussian distribution in general, whic...

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data to evaluate the functional connectivity, which assumes that the sources of functional networks are statistically independent. Recently, many researchers have demonstrated that sparsity is an effective assumption for fMRI signal separation. In this research, we present a sparse approxim...

Abstract: This paper provides a new statistical approach to blind recovery of both earth signal and source wavelet given only the seismic traces using independent component analysis (ICA) by explicitly exploiting the sparsity of both the reflectivity sequence and the mixing matrix. Our proposed blind seismic deconvolution algorithm consists of three steps. Firstly, a transformation method that ...

The task of enhancing the perception of a scene by combining information captured by different sensors is usually known as image fusion. The pyramid decomposition and the Dual-Tree Wavelet Transform have been thoroughly applied in image fusion as analysis and synthesis tools. Using a number of pixel-based and region-based fusion rules, one can combine the important features of the input images ...

In recent years, access to multimedia data has become much easier due to rapid growth of the internet. While this is usually considered an improvement of everyday life, it also makes unauthorized copying and distributing of multimedia data much easier, therefore presenting a field of watermarking.Many literatures have reported about Discrete Wavelet Transform (DWT) based watermarking for data s...

This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data set of corrupted surface EMG signals to create multidimensional signal. Afterward, independent component analysis (ICA) is used to separate ECG artifact componen...