Speech signal analysis is one of the important areas of research in multimedia applications. Discrete Wavelet technique is effectively reduces the unwanted higher or lower order frequency components in a speech signal. Wavelet-based algorithm for audio de-noising is worked out. We focused on audio signals corrupted with white Gaussian noise which is especially hard to remove because it is locat...

This paper describes the functionality in the JPEG 2000 Part 1 standard, for encoding images with predefined regions of interest (ROI) of arbitrary shape. The method described is called the Maxshift method. This method is based on scaling of the wavelet coefficients after the wavelet transformation and quantization. By sufficiently scaling the wavelet coefficients used to reconstruct the ROI, a...

Sub-pixel registration is a crucial step for applications such as super-resolution in remote sensing, motion compensation in magnetic resonance imaging, and non-destructive testing in manufacturing, to name a few. Recently, these technologies have been trending towards wavelet encoded imaging and sparse/compressive sensing. The former plays a crucial role in reducing imaging artifacts, while th...

This paper proposes a spatially adaptive statistical model for wavelet image coefficients in order to perform image de-noising. The wavelet coefficients are modelled as zero-mean Gaussian random variables with high local correlation. This model is developed in a Bayesian framework, where a Maximum Likelihood (ML) estimator evaluates the variance of the blocks to which the wavelet subbands have ...

An effective rotation and scale invariant log-polar wavelet texture feature for image retrieval was proposed. The feature extraction process involves a log-polar transform followed by an adaptive row shift invariant wavelet packet transform. The log-polar transform converts a given image into a rotation and scale invariant but rowshifted image, which is then passed to the adaptive row shift inv...

As a novel data mining approach, a wavelet entropy algorithm is used to perform entropy statistics on wavelet coefficients (or reconstructed signals) at various wavelet scales on the basis of wavelet decomposition and entropy statistic theory. Shannon wavelet energy entropy, one kind of wavelet entropy algorithm, has been taken into consideration and utilized in many areas since it came into be...

Currently, it appears that the best method for non-Gaussianity detection in the Cosmic Microwave Background (CMB) consists in calculating the kurtosis of the wavelet coefficients. We know that wavelet-kurtosis outperforms other methods such as the bispectrum, the genus, ridgelet-kurtosis and curvelet-kurtosis on an empirical basis, but relatively few studies have compared other transform-based ...

introduction: wavelet transform is one of the useful and suitable tools for time series and signal analysis. nowadays wavelet transform is frequently used in geophysical data processing and interpretation, especially seismic data. however, the use of this method isn’t widespread in gravity and geomagnetic. fedi and quarta (1998), martelet et al. (2001) and de oliveira lyrio (2004) used the wave...

The nonlinear dynamical system which models the axon impulse activity is studied through the analysis of the wavelet coefficients. A system with a high pulse source is analyzed in dependence of the amplitude. It is shown that there exists a critical value of the amplitude, and a catastrophe above it. This value is easily detected by the large values of the wavelet coefficients.

This paper describes a framework to estimate global motion model based on B-spline wavelets. The wavelet-based model allows optical flow to be recovered at different resolution levels from image derivatives. By combining estimation from different resolution levels in a coarse to fine scheme, our algorithm is able to recover a large range of velocity magnitude. Our algorithm is evaluated on arti...