Wavelet analysis is proposed as a new tool for studying the large-scale structure formation of the universe. To reveal its usefulness, the wavelet decomposition of one-dimensional cosmological density fluctuations is performed. In contrast with the Fourier analysis, the wavelet analysis has advantage of its ability to keep the information for location of local density peaks in addition to that ...

Wavelet thresholding is a powerful tool for denoising images and other signals with sharp discontinuities. Using different wavelet bases gives different results, and since the wavelet transform is not time-invariant, thresholding various shifts of the signal is one way to use different wavelet bases. This paper describes several denoising methods that apply wavelet thresholding or variations on...

This work presents a framework for wavelet-based representation of a given deterministic signal. We propose a method for matched wavelet based representation of the signal. Our method requires a signalconditioning step at each stage of the wavelet decomposition. The wavelet system designed in this work is biorthogonal. Both the signal-conditioning step and the wavelet system depend on the given...

Wavelet theory is one the greatest achievement of last decade. Wavelet theory has gained popularity in solving difficult problems in mathematics, engineering etc. It can be employed in lots of fields and applications, such as signal processing, image analysis, communication systems, time frequency analysis, image compression, smoothing and image denoising , pattern recognition, finger print ver...

Efficient image watermarking techniques have been developed in the wavelet domain. Similar to other wavelet-based image processing, the choice of wavelet filters generally affects the performance of a wavelet-based watermarking system. In this paper, we evaluate the performance of a set of biorthogonal integer wavelets under a multiresolution-watermarking framework. Biorthogonal integer wavelet...

If the wavelet system {aψ(a ·−bk)}j,k∈Z forms a frame onL(R) for some a > 1 and b > 0, then it is called a (regular) wavelet frame. In this case we can reconstruct any f from the sampled values (Wψf)(a , abk). In practice the sampling points may be irregular. We need to know for which wavelet ψ and parameters {(sj , bk)}j,k , the wavelet system {s j ψ(sj · −bk)}j,k∈Z forms a frame on L (R). In ...

In this paper a wavelet based model for image de-noising is presented. Wavelet coefficients are modelled as waves that grow while dilating along scales. The model establishes a precise link between corresponding modulus maxima in the wavelet domain and then allows to predict wavelet coefficients at each scale from the first one. This property combined with the theoretical results about the char...

Wavelet analysis has become a developing branch of mathematics for over twenty years. In this paper, the notion of orthogonal nonseparable bivariate wavelet packs, which is the generalization of orthogonal univariate wavelet packs, is proposed by virtue of analogy method and iteration method. Their biorthogonality traits are researched by using time-frequency analysis approach and variable sepa...

Wavelet denoising techniques are used to remove additive Gaussian noise by thresholding the wavelet coe cients. Like other transform based lters, wavelet shrinkage methods provide blur or visual artifacts that are exhibited in the neighborhood of image edges. The motive for implementing the Hybrid Wavelet Transform Filter (HWTF) is to provide discriminate smoothing operator for noise removal. T...

Wavelet, a powerful tool for signal processing, can be used to approximate the target function. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with better sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to...