We describe a formalism that allows us to study space (or time)-scale correlations in multiscale processes. This method, based on the continuous wavelet transform, is particularly well suited to study multiplicative r...
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ISBN:
(纸本)0819437646
We describe a formalism that allows us to study space (or time)-scale correlations in multiscale processes. This method, based on the continuous wavelet transform, is particularly well suited to study multiplicative random cascades for which the correlation functions take very simple expressions. This two-point space-scale statistical analysis is illustrated on synthetic multifractal signals and then applied to financial time series and fully developed turbulence data.
The optics of satellite cameras as well as the satellite movement create a blurring effect to which is added a noise due to the electronics of the CCD captors. We introduce a deconvolution algorithm which is regulariz...
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ISBN:
(纸本)0819422134
The optics of satellite cameras as well as the satellite movement create a blurring effect to which is added a noise due to the electronics of the CCD captors. We introduce a deconvolution algorithm which is regularized with a hard thresholding technic, in a wavelet frame. The restoration procedure is fast and provides good metrical and perceptual results.
Integer based-matrix algorithms for discrete Haar transform (DHT) and discrete wavelet transform (DWT) are proposed with relation to the multiresolution representation (MRR). A recursive wavelet transform technique is...
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ISBN:
(纸本)0819425915
Integer based-matrix algorithms for discrete Haar transform (DHT) and discrete wavelet transform (DWT) are proposed with relation to the multiresolution representation (MRR). A recursive wavelet transform technique is used with a view to demonstrating simply lossy reconstructed images in contrast to an original image under the specified resolution size. A visual effect of reconstructed images with differnt appearance and image quality, caused by modifying or throwing away a part of the 2-D HT or WT coefficients, is discussed with a measure of quantitative evaluation such as similarity and/or modified similarity, and fidelity RMSE and/or PSNR.
wavelet transform is a main tool for imageprocessingapplications in modern existence. A Double Density Dual Tree Discrete wavelet Transform is used and investigated for image denoising. images are considered for the...
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wavelet transform is a main tool for imageprocessingapplications in modern existence. A Double Density Dual Tree Discrete wavelet Transform is used and investigated for image denoising. images are considered for the analysis and the performance is compared with discrete wavelet transform and the Double Density DWT. Peak signal to Noise Ratio values and Root Means Square error are calculated in all the three wavelet techniques for denoised images and the performance has evaluated. The proposed techniques give the better performance when comparing other two wavelet techniques.
The method in which the wavelet transform and fractal theories are applied to detect chaotic signals with the additive observed noises is presented in this paper. Smoothing operator is derived from wavelet transform a...
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ISBN:
(纸本)0819422134
The method in which the wavelet transform and fractal theories are applied to detect chaotic signals with the additive observed noises is presented in this paper. Smoothing operator is derived from wavelet transform and used to process the chaotic time series. According to its character of no-scale interval in certain scales, a detected example of the noisy chaotic behavior produced by a Lorenz attractor is provided by using the improved G-P algorithm to calculate its fractal dimension. The results shows that the method introduced here expresses a good ability to detect chaos.
Transform methods have played an important role in signal and imageprocessingapplications. Recently, Selesnick has constructed the new orthogonal discrete wavelet transform, called the slantlet wavelet, with two zer...
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ISBN:
(纸本)0819452017
Transform methods have played an important role in signal and imageprocessingapplications. Recently, Selesnick has constructed the new orthogonal discrete wavelet transform, called the slantlet wavelet, with two zero moments and with improved time localization. The discrete slantlet wavelet transform is carried out by an existing filterbank which lacks a tree structure and has a complexity problem. The slantlet wavelet has been successfully applied in compression and denoising. In this paper, we present a new class of orthogonal parametric fast Haar slantlet transform system where the slantlet wavelet and Haar transforms are special cases of it. We propose designing the slantlet wavelet transform using Haar slantlet transform matrix. A new class of parametric filterbanks is developed. The behavior of the parametric Haar slantlet transforms in signal and image denoising is presented. We show that the new technique performs better than the slantlet wavelet transform in denoising for piecewise constant signals. We also show that the parametric Haar slantlet transform performs better than the cosine and Fourier transforms for grey level images.
The automatic analysis of Ground Penetrating Radar (GPR) images is an interesting topic in remote sensing imageprocessing, since it involves the use of pre-processing, detection and classification tools with the aim ...
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ISBN:
(纸本)0819442666
The automatic analysis of Ground Penetrating Radar (GPR) images is an interesting topic in remote sensing imageprocessing, since it involves the use of pre-processing, detection and classification tools with the aim of near-real time or at least very fast data interpretation. However, actual chains of preprocessing tools for GPR images do not consider usually denoising, essentially because most of the successive data interpretation is based on single radar trace analysis. So, no speckle noise analysis and denoising has been attempted, perhaps assuming that this point is immaterial for the following interpretation or detection tools. Instead, we expect that speckle denoising procedures would help. In this paper we address this problem, providing a detailed and exhaustive comparison of many of the statistical algorithms for speckle reduction provided in literature, i.e. Kuan, Lee, Median, Oddy and wavelet filters. For a more precise comparison, we use the Equivalent Number of Look (ENL), the Variance Ratio (VR). Moreover, we validate the denoising results by applying an interpretation step to the pre-processed data. We show that a wavelet denoising procedure results in a large improvement for both the ENL and VR. Moreover, it also allows the neural detector to individuate more targets and less false positive in the same GPR data set.
In this paper, we consider classes of not bandlimited signals, namely, streams of Diracs and piecewise polynomial signals, and show that these signals can be sampled and perfectly reconstructed using wavelets as sampl...
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ISBN:
(纸本)0819450804
In this paper, we consider classes of not bandlimited signals, namely, streams of Diracs and piecewise polynomial signals, and show that these signals can be sampled and perfectly reconstructed using wavelets as sampling kernel. Due to the multiresolution structure of the wavelet transform, these new sampling theorems naturally lead to the development of a new resolution enhancement algorithm based on wavelet footprints.(2) Preliminary results show that this algorithm is also very resilient to noise.
In this paper, we study unsupervised image segmentation using wavelet-domain hidden Markov models (HMMs), where three clustering methods are used to obtain the initial segmentation results. We first review recent supe...
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ISBN:
(纸本)0819450804
In this paper, we study unsupervised image segmentation using wavelet-domain hidden Markov models (HMMs), where three clustering methods are used to obtain the initial segmentation results. We first review recent supervised Bayesian image segmentation algorithms using wavelet-domain HMMs. Then, a new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs. Three clustering methods, i.e., K-mean, soft clustering and multiscale clustering, are studied to convert the unsupervised segmentation problem into the self-supervised process by identifying the reliable training samples. The simulation results on synthetic mosaics and real images show that the proposed unsupervised segmentation algorithms can achieve high classification accuracy.
We present a unified image compressor with spline biorthogonal wavelets and dyadic rational filter coefficients which gives high computational speed and excellent compression performance. Convolutions with these filte...
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