image denoising is an active area of research and probably one of the most studied problems in the imageprocessing fields. In this paper we describe a new hybrid image denoising algorithm which combines Gaussian base...
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ISBN:
(纸本)9781479902699;9781479902675
image denoising is an active area of research and probably one of the most studied problems in the imageprocessing fields. In this paper we describe a new hybrid image denoising algorithm which combines Gaussian based neighborhood spatial filter with wavelet transform that based on neighborhood thresholding function which takes the correlation of the magnitude of the wavelet coefficient with its neighbors into consideration to decide whether the coefficient is noisy or noise free. Accordingly, noises are detected with the help of the surrounding information and are removed. Experimental results show that the proposed algorithm can effectively remove the image noises with less processing time as compared with the state-of-the-art denoising algorithm.
The basic objective of this paper is to analyze the concept of wavelet based algorithms for image compression using different parameter. All algorithms are based on still images, The algorithm involved in the comparat...
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ISBN:
(纸本)9781479933587
The basic objective of this paper is to analyze the concept of wavelet based algorithms for image compression using different parameter. All algorithms are based on still images, The algorithm involved in the comparative analysis is wavelet Difference Reduction (WDR), Spatial orientation tree wavelet (STW), Embedded zero tree wavelet (EZW) and modified Set Partitioning in hierarchical trees (SPIHT). These algorithms are more effective and deliver a better feature in the image. In compression, wavelets transform have shown a good elasticity to a large amount of data, while being of realistic complexity. These techniques are used in many imageprocessingapplications. The techniques are compared by using the performance parameters peak signal to noise ratio (PSNR) & mean square error (MSE).
Maximum likelihood detectors of narrowband, non-stationary random echos in Gaussian noise can be efficiently implemented in the time-frequency domain. When the transmitted signals have large time-bandwidth products, t...
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ISBN:
(纸本)0819425915
Maximum likelihood detectors of narrowband, non-stationary random echos in Gaussian noise can be efficiently implemented in the time-frequency domain. When the transmitted signals have large time-bandwidth products, the natural implementation of estimators and detectors is the time-scale or wavelet transform domain implementation. This paper extends the wavelet transform implementations to include weighted time-frequency or time-scale (TF/TS) transforms. We define weighted TF/TS transforms using Reproducing Kernel Hilbert Space (RKHS) inner products. Inverses of these weighted TF/TS transforms are also given. The particular case of the weight being the inverse noise covariance is presented. We show how weighted transforms are used in the estimator-correlator detection statistic for complex scattering environments in conjunction with cascaded scattering functions so that the resulting detection statistic is much more robust. The weighted TF/TS transform turns out to be a natural transform for solving nonstationary detection, estimation, and filtering problems and has important applications to transient signal estimation in multipath channels with colored non-stationary Gaussian noise.
Atrial fibrillation (AF) is a common arrhythmia associated with many heart diseases and has a high rate of incidence in the older population. Many of the symptoms of AF are poorly tolerated by patients and if not prop...
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ISBN:
(纸本)0819425915
Atrial fibrillation (AF) is a common arrhythmia associated with many heart diseases and has a high rate of incidence in the older population. Many of the symptoms of AF are poorly tolerated by patients and if not properly managed, may lead to fatal conditions like embolic stroke. The atrial electrograms during AF show a high degree of non-stationarity AF being progressive in nature, we aim to link the the degree of non-stationarity of the atrial electrogram to the stage of advancement of the disease, the duration of episodes of AF, possibility of spontaneous reversion to sinus rhythm and the defibrillation energy requirement. In this paper we describe a novel algorithm for classifying bipolar electrograms from the right atrium of sheep into four groups - normal sinus rhythm, atrial flutter, paroxysmal AF, chronic AF. This algorithm uses features derived from a wavelet transform representation of the signal to train an artificial neural network which is then used to classify the different arrhythmia. The success rates achieved for each subclass indicates that this approach is well suited for the study of atrial arrhythmia.
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 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.
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.
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.
The statistics of photographic images, when decomposed in a multiscale wavelet basis, exhibit striking nonGaussian behaviors. The joint densities of dusters of wavelet coefficients (corresponding to basis functions at...
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ISBN:
(纸本)0819437646
The statistics of photographic images, when decomposed in a multiscale wavelet basis, exhibit striking nonGaussian behaviors. The joint densities of dusters of wavelet coefficients (corresponding to basis functions at nearby spatial positions, orientations and scales) are well-described as a Gaussian scale mixture: a jointly Gaussian vector multiplied by a hidden scaling variable. We develop a maximum likelihood solution for estimating the hidden variable from an observation of the cluster of coefficients contaminated by additive Gaussian noise. The estimated hidden variable is then used to estimate the original noise-free coefficients. We demonstrate the power of this model through numerical simulations of image denoising.
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