In this paper, we compare Fourier-based and wavelet-based denoising techniques applied to both synthetic and real experimental geophysical data. The Fourier-based technique used for comparison is the classical Wiener estimator, and the wavelet-based techniques tested include soft and hard wavelet thresholding and the empirical Bayes (EB) method. Both real and synthetic data sets were used to co...

The speckle corrupted image is a traditional problem in both biomedical and in synthetic aperture processing applications, including synthetic aperture radar (SAR). In a SAR image, speckle manifests itself in the form of a random pixel-to-pixel variation with statistical properties similar to those of thermal noise. Due to its granular appearance in an image, speckle noise makes it very difficu...

This paper about to reduce the noise by Adaptive time-frequency Block Thresholding procedure using discrete wavelet transform to achieve better SNR of the audio signal. Discrete-wavelet transforms based algorithms are used for audio signal denoising. The resulting algorithm is robust to variations of signal structures such as short transients and long harmonics. Analysis is done on noisy speech...

Wavelet analysis as a recently data filtering method (or multi-scale decomposition) is particularly useful for describing signals with sharp spiky, discontinuous or fractal structure in financial markets. This study investigates the optimal several wavelet thresholding criteria or techniques to support the multi-signal decomposition methods of a daily Korean won / U.S. dollar currency market as...

The denoising of a natural image corrupted by additive white Gaussian noise (AWGN) is a classical problem in the signal processing community. The corruption of an image by noise is common during its acquisition or transmission. The aim of denoising is to remove the noise while keeping the signal featuresas much as possible. Traditional algorithms, such as the standard median (SM) filter and mea...

One of the most challenging tasks for which EMD could be useful is that of non-parametric signal denoising, an area in which wavelet thresholding has been the dominant technique for many years. In this paper, the major wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show, that although a direct application of this principle in the EM...

Abstract –Wavelet packets have been found to be effective in denoising of biological signals. Wavelet based denoising methods widely employ hard and soft thresholding filters for denoising the signals. This paper introduces a New thresholding filter for the purpose of thresholding in denoisng of EEG signals using wavelet packets. The functioning of the filter is examined and compared with that ...

Wavelet thresholding is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. But the choice of thresholding function has restricted there wide spread use in image denoising application. In this paper we proposed a computationally more efficient thresholding scheme by incorporating the neighbouring wavelet coefficients, with different threshold ...

We investigate Wiener filtering of wavelet coefficients for signal denoising. Empirically designed wavelet-domain Wiener filters have superior performance over other denoising algorithms using wavelet thresholding. However, it is not clear how we should choose the signal model that is used to design the filter, because the effect of model selection on the filter performance was hard to understa...