Noise is one of the most widespread problems present in nearly all imaging applications. The search for efficient image denoising methods is still a valid challenge. In spite of the sophistication of the recently prop...
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ISBN:
(纸本)9781467358057
Noise is one of the most widespread problems present in nearly all imaging applications. The search for efficient image denoising methods is still a valid challenge. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. They all show an outstanding performance when the image model corresponds to the algorithm assumptions, but fail in general and create artifacts or remove fine image structures. Therefore, a universal "best" filter has yet to be found. wavelet analysis is a new method consisting of a set basis functions that can be used to analyze signals in both time (or space) and frequency domains simultaneously. In this paper, a novel hybrid filter for image despeckling that combines wavelet denoising and an enhanced adaptive Kuan filter is proposed, resulting in a significant gain with respect to many spatial as well as wavelet-based speckle reduction filters.
In this paper, after reviewing a general model to deal with signal-dependent image noise, the well known Local Linear Minimum Mean Squared Error (LLMMSE) filter is derived for the most general case. signal-dependent n...
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ISBN:
(纸本)0819437646
In this paper, after reviewing a general model to deal with signal-dependent image noise, the well known Local Linear Minimum Mean Squared Error (LLMMSE) filter is derived for the most general case. signal-dependent noise filtering is approached in a multiresolution framework either by LLMMSE processing ratios of combinations of lowpass images, which are tailored to the noise model in order to mitigate its signal-dependence, or by thresholding a normalized nonredundant wavelet transform designed to yield signal-independent noisy coefficients as well. Experimental results demonstrate that the Laplacian pyramid approach largely outperform LLMMSE filtering on a unique scale and is still superior to wavelet denoising by soft-thresholding.
In this paper we show that if wavelet domain processing is used with digital restoration, then pixel-scale features can be restored exactly in the absence of noise. In the presence of noise results are similar, except...
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ISBN:
(纸本)0819425915
In this paper we show that if wavelet domain processing is used with digital restoration, then pixel-scale features can be restored exactly in the absence of noise. In the presence of noise results are similar, except for some noise-amplification and ringing artifacts. wavelet domain modeling eliminates the need to discretize the image acquisition kernel and helps formulate image restoration as a discrete least squares problem. The performance of this technique is analyzed by model-based simulation using a comprehensive model to account for system blur at the image formation level, for the potentially important effects of aliasing, and for additive noise.
We present a viewpoint of studying biorthogonal wavelets by using wavelet operators. A characterization of MRA biorthogonal wavelets is given in the framework of wavelet operators. An efficient wavelet filtering algor...
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ISBN:
(纸本)0819425915
We present a viewpoint of studying biorthogonal wavelets by using wavelet operators. A characterization of MRA biorthogonal wavelets is given in the framework of wavelet operators. An efficient wavelet filtering algorithm based on this characterization is applied to X-ray computerized tomography (CT) for multiresolution reconstruction and reduced X-ray exposure. Simulation results indicate that wavelet based reconstruction allows satisfactory image quality in a region of interest from local wavelet and global scaling components of projection data. The results are directly applicable to medical X-ray CT.
image coding using multirate filter banks and wavelets is evaluated in this paper. The coding systems considered are based on the M-channel general lapped biorthogonal transform (GLBT) and the embedded zerotree wavele...
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ISBN:
(纸本)0780374029
image coding using multirate filter banks and wavelets is evaluated in this paper. The coding systems considered are based on the M-channel general lapped biorthogonal transform (GLBT) and the embedded zerotree wavelet (EZW) coding methods, as well as the baseline JPEG standard. The study concentrates on both coding efficiency and complexity. The tradeoff between efficiency and complexity of each coding system has been analyzed. The coding results show that the choice of a coding scheme depends mainly on the applications at hand.
In this paper, we propose to use secret, key-dependent parametric wavelet filters to improve the security of digital watermarking schemes operating in the wavelet transform domain We show that the parametrization of w...
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ISBN:
(纸本)0780367251
In this paper, we propose to use secret, key-dependent parametric wavelet filters to improve the security of digital watermarking schemes operating in the wavelet transform domain We show that the parametrization of wavelet filters can be easily integrated into existing wavelet-based watermarking algorithms, resulting in improved security without additional computational complexity. Both, robustness and imperceptibility are adequate for many applications.
If G is an orthonormal system in IL2 then for any function g is an element of G the function g(2) is a probability density. In this paper we discuss the properties of wavelet based densities and corresponding random v...
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ISBN:
(纸本)0819425915
If G is an orthonormal system in IL2 then for any function g is an element of G the function g(2) is a probability density. In this paper we discuss the properties of wavelet based densities and corresponding random variables.
Most of the noise models encountered in signalprocessing are either additive or multiplicative. However, the widely held wavelet shrinkage estimators for signal denoising deal only with additive noise. We propose a B...
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ISBN:
(纸本)0780376226
Most of the noise models encountered in signalprocessing are either additive or multiplicative. However, the widely held wavelet shrinkage estimators for signal denoising deal only with additive noise. We propose a Bayesian wavelet shrinkage model that encompasses both types of noise as well as noise that may exist between these two extremes. In applications such as SAR imaging, where multiplicative noise is predominant, statistical models intended for additive noise removal can effect a fair amount of restoration. This leads us to believe that noise in the signal can be considered as somewhere between multiplicative and additive. The new estimator removes noise by better adapting to the noise on hand. This approach is motivated by the, work of Pericchi [I] on the analysis of Box & Cox [2] transformations in the linear model. In addition, mixture priors governing the transformation are shown to be useful in predicting the noise from a choice of models. Experimental results are also reported.
In signalprocessing, the region of abrupt changes contains the most of the useful information about the nature of the signal. The region or the points where these changes occurred are often termed as singular point o...
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ISBN:
(纸本)9780819479280
In signalprocessing, the region of abrupt changes contains the most of the useful information about the nature of the signal. The region or the points where these changes occurred are often termed as singular point or singular region. The singularity is considered to be an important character of the signal, as it refers to the discontinuity and interruption present in the signal and the main purpose of the detection of such singular point is to identify the existence, location and size of those singularities. Electrocardiogram (ECG) signal is used to analyze the cardiovascular activity in the human body. However the presence of noise due to several reasons limits the doctor's decision and prevents accurate identification of different pathologies. In this work we attempt to analyze the ECG signal with energy based approach and some heuristic methods to segment and identify different signatures inside the signal. ECG signal has been initially denoised by empirical wavelet shrinkage approach based on Steins Unbiased Risk Estimate (SURE). At the second stage, the ECG signal has been analyzed by Mallat approach based on modulus maximas and Lipschitz exponent computation. The results from both approaches has been discussed and important aspects has been highlighted. In order to evaluate the algorithm, the analysis has been done on MIT-BIH Arrhythmia database;a set of ECG data records sampled at a rate of 360 Hz with 11 bit resolution over a 10mv range. The results have been examined and approved by medical doctors.
We present planar curve descriptors that allow a hierarchical representation of curves. The descriptors are based on wavelet and multiwavelet transforms and they decompose a curve into components of different scales.
ISBN:
(纸本)0819429139
We present planar curve descriptors that allow a hierarchical representation of curves. The descriptors are based on wavelet and multiwavelet transforms and they decompose a curve into components of different scales.
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