This paper aims at reviewing the recent published works dealing with industrial applications of wavelet and, more generally speaking, multiresolution analysis. After a quick recall in a simple overview of the basics o...
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ISBN:
(纸本)0819455601
This paper aims at reviewing the recent published works dealing with industrial applications of wavelet and, more generally speaking, multiresolution analysis. After a quick recall in a simple overview of the basics of wavelet transform and of its main variations, some of its applications are reviewed domain by domain, beginning with signalprocessing, continuous and discrete wavelet transform proceeding with imageprocessing and applications. More than 150 recent papers are presented in these two sections.
Semifragile watermarking techniques aim to prevent tampering and fraudulent use of modified images. A semifragile watermark monitors the integrity of the content of the image but not its numerical representation. Ther...
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Semifragile watermarking techniques aim to prevent tampering and fraudulent use of modified images. A semifragile watermark monitors the integrity of the content of the image but not its numerical representation. Therefore, the watermark is designed so that the integrity is proven if the content of the image has not been tampered with, despite some mild processing on the image. However, if parts of the image are replaced with the wrong key or are heavily processed, the watermark information should indicate evidence of forgery. We compare the performance of eight semifragile watermarking algorithms in terms of their miss probability under forgery attack, and in terms of false alarm probability under nonmalicious signalprocessing operations that preserve the content and quality of the image. We propose desiderata for semifragile watermarking algorithms and indicate the promising algorithms among existing ones. (C) 2004 SPIE and IST.
Recovering a pattern or image from a collection of noisy And misaligned observations is a challenging problem that arises in imageprocessing and pattern recognition. This. paper presents an automatic, wavelet-based a...
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Recovering a pattern or image from a collection of noisy And misaligned observations is a challenging problem that arises in imageprocessing and pattern recognition. This. paper presents an automatic, wavelet-based approach to this problem. Despite the success of wavelet decompositions in other areas of statistical signal and imageprocessing, most wavelet-based image models are inadequate for modeling patterns in images, due to the presence of unknown transformations (e.g., translation, rotation, location of lighting source) inherent in pattern observations. Our framework takes advantage of the efficient image representations afforded by wavelets while accounting for unknown translations and rotations. In order to learn the parameters of our model from training data, we introduce Template Learning from Atomic Representations (TEMPLAR): a novel template learning algorithm. The problem solved by TEMPLAR is the recovery of a pattern template from a collection of noisy, randomly translated, and rotated observations of the pattern. TEMPLAR employs minimum description length (MDL) complexity regularization to learn a template with a sparse representation in the wavelet domain. We discuss several applications, including template learning, pattern classification, and image registration.
This article presents the construction and various properties of complex Daubechies wavelets with a special emphasis on symmetric solutions. Such solutions exhibit interesting relationships between the real and imagin...
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This article presents the construction and various properties of complex Daubechies wavelets with a special emphasis on symmetric solutions. Such solutions exhibit interesting relationships between the real and imaginary components of the complex scaling function and the complex wavelet. We present those properties in the context of imageprocessing. Within the framework of statistical modelling, we focus on the redundant description of real images given by the complex multiresolution representation. A hierarchical Markovian Graphical model is then explored. We present an Expectation Maximization algorithm for optimizing the model with observational complex wavelet data. This model is then applied to image estimation and texture classification. In both applications, we demonstrate the benefit brought by the Markovian hypothesis and the performance of the real images's complex multiscale representation. (C) 2003 Elsevier B.V. All rights reserved.
One of the main applications of guided waves is for pipe inspection, since its one-dimensional geometry allows inspecting long distances in a short time. As with most configurations (frequency, thickness) ultrasonic w...
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One of the main applications of guided waves is for pipe inspection, since its one-dimensional geometry allows inspecting long distances in a short time. As with most configurations (frequency, thickness) ultrasonic waves with many modes of propagation are generated, recent research focuses mostly on the generation and reception of specific modes, mainly by means of sophisticated arrays of transducers. In this paper this problem is addressed with a different approach, by processing ultrasonic signals with a low signal/noise ratio acquired with a single transducer in an pulse-echo configuration. In order to improve the evaluation of results, frequency bandpass filters and wavelet analysis were tested. Results showed that even when very noisy signals are utilized, signalprocessing improve the signal/noise (S/N) ratio up to 12 dB approximately and enhance the analysis of the results, thus demonstrating its usefulness. (C) 2004 Elsevier B.V. All rights reserved.
A color image compression and reconstruction method based on fuzzy wavelets is proposed, where a soft thresholding scheme can be formulated as an extension of alpha-cut of fuzzy relation. The proposed fuzzy wavelets a...
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ISBN:
(纸本)0889864349
A color image compression and reconstruction method based on fuzzy wavelets is proposed, where a soft thresholding scheme can be formulated as an extension of alpha-cut of fuzzy relation. The proposed fuzzy wavelets are based on a partial fuzzification of morphological wavelets and the image compression and reconstruction process can be adjusted by fuzzy sets that correspond to soft threshold. Therefore,the proposed fuzzy wavelets are more natural interpretation of fuzzy wavelets and a human subjectivity can be applied to the image compression process, compared with the conventional fuzzy wavelets. Through an experiment using a color image extracted from Standard image DataBAse (SIDBA), it is confirmed that the root mean square error of the reconstructed image obtained by the soft thresholding is decreased to 84.79% of that of the hard thredholding, under the condition that the compression rate is about 0.06.
The maximum-entropy method (MEM) is often used for enhancing astronomical images and, in particular, has recently been applied to cosmic microwave background (CMB) observations. Wavelet functions are also now used wid...
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The maximum-entropy method (MEM) is often used for enhancing astronomical images and, in particular, has recently been applied to cosmic microwave background (CMB) observations. Wavelet functions are also now used widely in astronomy, since they allow the sparse and efficient representation of a signal at different scales, and the application of wavelets to the denoising of CMB maps has been investigated. In this paper, we give a systematic discussion of how to combine these two approaches by the use of the MEM in wavelet bases for the denoising and deconvolution of general images and, in particular, CMB maps. We find that the MEM in the a trous wavelet basis has lower reconstruction residuals than conventional pixel-basis MEM in the case when the signal-to-noise ratio is low and the point spread function is narrow. Furthermore, the Bayesian evidence for the wavelet MEM reconstructions is generally higher for a wide range of images. From a Bayesian point of view, the wavelet basis thus provides a better model of the image.
In this paper the new family of orthogonal compactly supported M-band wavelets is presented. This family is similar to Coiflets since scaling function and all wavelet functions have equal number of vanishing moments. ...
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ISBN:
(纸本)0819450812
In this paper the new family of orthogonal compactly supported M-band wavelets is presented. This family is similar to Coiflets since scaling function and all wavelet functions have equal number of vanishing moments. However, the center of these vanishing moments can differ from zero. Equivalent moment conditions are given also for scaling filter. Finally, estimate for asymptotic convergence speed of wavelet sampling approximation of smooth functions is given.
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