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Bayesian Modeling in the Wavelet Domain
, 2004
"... Wavelets are the building blocks of wavelet transforms the same way that the functions e^inx are the building blocks of the ordinary Fourier transform. But in contrast to sines and cosines, wavelets can be (or almost can be) supported on an arbitrarily small closed interval. This feature makes wavel ..."
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Wavelets are the building blocks of wavelet transforms the same way that the functions e^inx are the building blocks of the ordinary Fourier transform. But in contrast to sines and cosines, wavelets can be (or almost can be) supported on an arbitrarily small closed interval. This feature makes
Wavelet Domain Inversion and Joint
, 2003
"... This thesis presents two innovations to geophysical inversion. The first provides a framework and an algorithm for combining linear deconvolution methods with geostatistical interpolation techniques. This allows for sparsely sampled data to aid in image deblurring problems, or, conversely, noisy an ..."
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first on a synthetic problem involving satellite remotely sensed data, and then on a real 3D seismic data set combined with well logs. The second innovation addresses how to use wavelets in a linear geophysical inverse problem. Wavelets have lead to great successes in image compression and denoising
PROPERTIES OF RANDOM S IGNALS IN WAVELET DOMAIN
"... I. Int rod uct io n1) The wavelet transform have been used mainly in the fields of signal processing, image coding and compression,and in certain areas of mathematics, as in solution of partial differential equations or numerical analysis[1][2][3][4]. Recently an enormous interest has emerged on the ..."
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on the use of wavelet transforms in several areas. One of these areas is to understand the statistical behavior of random signals in wavelet domain. Basseville et al.[5] studied random processes defined on a multiscale grid of wavelet decomposition coefficients but its relationship to conventional notions
Modeling Network Traffic in Wavelet Domain
, 1999
"... A significant discovery from this work is that although network traffic has the complicated short and longrange temporal dependence, the corresponding wavelet coefficients are no longer longrange dependent. Therefore, a "shortrange" dependent process can be used to model network traffi ..."
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Cited by 1 (1 self)
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traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model which captures additional correlations
Modeling Video Traffic in The Wavelet Domain
, 1998
"... A significant discovery from this work is that although video traffic has complicated short and longrange dependence in the time domain, the corresponding wavelet coefficients are no longer longrange dependent in the wavelet domain. Therefore, a "shortrange" dependent process can be use ..."
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Cited by 20 (6 self)
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A significant discovery from this work is that although video traffic has complicated short and longrange dependence in the time domain, the corresponding wavelet coefficients are no longer longrange dependent in the wavelet domain. Therefore, a "shortrange" dependent process can
ECG Statistical Denoising in the Wavelet Domain
"... Abstract – The paper presents a denoising algorithm particularly suited to ECG signals processing. The main stage of this algorithm consists in a MAP filtering in the wavelet domain. Its effectiveness relies on the qualities of the wavelet transform and of the statistical filter used. Tests made on ..."
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Abstract – The paper presents a denoising algorithm particularly suited to ECG signals processing. The main stage of this algorithm consists in a MAP filtering in the wavelet domain. Its effectiveness relies on the qualities of the wavelet transform and of the statistical filter used. Tests made
Wavelet Domain Blind Image Separation
 in SPIE, Mathematical Modeling, Wavelets X
, 2003
"... In this work, we consider the problem of blind source separation in the wavelet domain via a Bayesian estimation framework. We use the sparsity and multiresolution properties of the wavelet coe#cients to model their distribution by heavy tailed prior probability laws: the generalized exponential fam ..."
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In this work, we consider the problem of blind source separation in the wavelet domain via a Bayesian estimation framework. We use the sparsity and multiresolution properties of the wavelet coe#cients to model their distribution by heavy tailed prior probability laws: the generalized exponential
Image compression via joint statistical characterization in the wavelet domain
, 1997
"... We develop a statistical characterization of natural images in the wavelet transform domain. This characterization describes the joint statistics between pairs of subband coefficients at adjacent spatial locations, orientations, and scales. We observe that the raw coefficients are nearly decorrelate ..."
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Cited by 237 (24 self)
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We develop a statistical characterization of natural images in the wavelet transform domain. This characterization describes the joint statistics between pairs of subband coefficients at adjacent spatial locations, orientations, and scales. We observe that the raw coefficients are nearly
Image Mirroring and Rotation in the Wavelet Domain
"... Abstract—JPEG2000, the international standard for still image compression, uses wavelet transform. The filters used by JPEG2000 for transformation are the 9/7 Daubechies filters and the 5/3 Le Gall filter. At present, to achieve image mirroring or rotation for images, we have to convert them back t ..."
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to spatial domain and proceed. This paper presents methods by which we can implement the same in the wavelet domain, without extra computational complexity. To do so, we manipulate the transform domain coefficients so that the condition of perfect reconstruction still holds true. Index Terms
An EM Algorithm to Learn Sequences in the Wavelet Domain
"... D. H. Milone & L. Di Persia; "An EM algorithm to learn sequences in the wavelet domain" ..."
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D. H. Milone & L. Di Persia; "An EM algorithm to learn sequences in the wavelet domain"
Results 11  20
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