Although the discrete wavelet transform (DWT) is a powerful tool for signal and imageprocessing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome ...
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Although the discrete wavelet transform (DWT) is a powerful tool for signal and imageprocessing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce multidimensional, mapping-based, complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, nonredundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and nonredundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we exploit this flexibility to create the complex double-density DWT: a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3(M) - 1)1(2(M) - 1) in M dimensions. No other transform achieves all these properties at a lower redundancy, to the best of our knowledge. By exploiting the advantages of our multidimensional, mapping-based complex wavelet transforms in seismic signal-processingapplications, we have demonstrated state-of-the-art results.
For visual processingapplications, the two-dimensional (2-D) Discrete wavelet Transform (DWT) can be used to decompose an image into four-subband images. However, when a single band is required for a specific applica...
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For visual processingapplications, the two-dimensional (2-D) Discrete wavelet Transform (DWT) can be used to decompose an image into four-subband images. However, when a single band is required for a specific application, the four-band decomposition demands a huge complexity and transpose time. This work presents a fast algorithm, namely 2-D Symmetric Mask-based Discrete wavelet Transform (SMDWT), to address some critical issues of the 2-D DWT. Unlike the traditional DWT involving dependent decompositions, the SMDWT itself is subband processing independent, which can significantly reduce complexity. Moreover, DWT cannot directly obtain target subbands as mentioned, which leads to an extra wasting in transpose memory, critical path, and operation time. These problems can be fully improved with the proposed SMDWT. Nowadays, many applications employ DWT as the core transformation approach, the problems indicated above have motivated researchers to develop lower complexity schemes for DWT. The proposed SMDWT has been proved as a highly efficient and independent processing to yield target subbands, which can be applied to real-time visual applications, such as moving object detection and tracking, texture segmentation, image/video compression, and any possible DWT-based applications. (C) 2011 Elsevier B.V. All rights reserved.
In this paper we present an overview of the various uses of the wavelet transform (WT) in medicine and biology. We start by describing the wavelet properties that are the most important for biomedical applications. In...
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In this paper we present an overview of the various uses of the wavelet transform (WT) in medicine and biology. We start by describing the wavelet properties that are the most important for biomedical applications. In particular, we provide an interpretation of the continuous wavelet transform (CWT) as a prewhitening multiscale matched filter. Me also briefly indicate the analogy between the WT and some of the biological processing that occurs in the early components of the auditory and visual system. We then review the rises of the WT for the analysis of 1-D physiological signals obtained by phonocardiography, electrocardiography (ECC), and electroencephalography (EEG), including evoked response Next, we provide a survey of recent wavelet developments in medical imaging. These include biomedical imageprocessing algorithms (e.g., noise reduction, image enhancement, and detection of microcalcifications in mammograms), image reconstruction and acquisition schemes (tomography, and magnetic resonance imaging (MRI)), and multiresolution methods for the registration and statistical analysis of functional images of the brain (positron emission tomography (PET) and functional MRI (fMRI)). In each case, we provide the reader with some general background information and a brief explanation of how the methods work.
This paper is to evaluate the importance of image preprocessing using multiresolution and multiorientation wavelet transforms (WTs) on the performance of a previously reported computer assisted diagnostic (CAD) method...
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ISBN:
(纸本)0819437646
This paper is to evaluate the importance of image preprocessing using multiresolution and multiorientation wavelet transforms (WTs) on the performance of a previously reported computer assisted diagnostic (CAD) method for breast cancer screening, using digital mammography. An analysis of the influence of WTs on image feature extraction for mass detection is achieved by comparing the discriminate ability of features extracted with and without wavelet based image preprocessing using computed ROC. Three indexes are proposed to assess the segmentation of the mass area with comparison to ground truth. Data was analyzed on region-of-interest (ROI) database that included mass and normal regions from digitized mammograms with ground truth. The metrics for measurement of segmentation of the mass clearly demonstrates the importance of image preprocessing methods. Similarly, the relative improvement in performance is observed in feature extraction, where the Az values are increased. The improvement depends on the feature characteristics. The use of methodology in this paper results in a significant improvement in feature extraction for the previously proposed CAD detection method. We are therefore exploring additional improvement in wavelet based image preprocessing methods, including adaptive methods, to achieve a further improvement in performance on larger image databases.
The problem of recovering an input signal from noisy and linearly distorted data arises in many different areas of scientific investigation;e.g., noisy Radon inversion (tomography) is a problem of special interest and...
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ISBN:
(纸本)0819437646
The problem of recovering an input signal from noisy and linearly distorted data arises in many different areas of scientific investigation;e.g., noisy Radon inversion (tomography) is a problem of special interest and considerable practical relevance in medical imaging. We will argue that traditional methods for solving inverse problems - damping of the singular value decomposition (SVD) or cognate methods - behave poorly when the object to recover has edges. We apply a new system of representation, namely, the curvelets in this setting. Curvelets provide near-optimal representations of otherwise smooth objects with discontinuities along smooth C-2 edges. Inspired by some recent work on nonlinear estimation, we construct a curvelet-based biorthogonal decomposition of the Radon operator and build a reconstruction based on the shrinkage (or thresholding) of the noisy curvelet coefficients. This novel approach is shown to give a new theoretical understanding of the problem of edges in the Radon inversion problem.
wavelet transforms have proven to be useful tools for several applications, including signal analysis, signal compression and numerical analysis. This paper surveys the VLSI architectures that have been proposed for c...
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wavelet transforms have proven to be useful tools for several applications, including signal analysis, signal compression and numerical analysis. This paper surveys the VLSI architectures that have been proposed for computing the Discrete and Continuous wavelet Transforms for I-D and 2-D signals. The architectures are based upon on-line versions of the wavelet transform algorithms. These architectures support single chip implementations and are optimal with respect to both area and time under the word-serial model.
We present a method for accurate estimation of formant frequencies. The method is based on differentiating the phase of the short time Fourier transform. The motivation for the method is its application to the estimat...
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ISBN:
(纸本)0819437646
We present a method for accurate estimation of formant frequencies. The method is based on differentiating the phase of the short time Fourier transform. The motivation for the method is its application to the estimation of the recently introduced "universal warping function" which is aimed at separating the speaker dependence from the phonetic content of a speech utterance. The universal warping function is determined by the nature of the relationship between formants of different speakers for phonetically similar sounds and requires an accurate estimate of formants, The proposed method provides sufficiently accuracy for its estimation.
wavelet analysis has been widely applied in many fields such as imageprocessing as well as signal representation and analysis with its unique characteristics of time-frequency localization. To use multi-wavelet in th...
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wavelet analysis has been widely applied in many fields such as imageprocessing as well as signal representation and analysis with its unique characteristics of time-frequency localization. To use multi-wavelet in the image compression is an important aspect of the application of wavelet. However, most of the multi-scale function of the existing wavelet can meet the low-pass property and how to convert one-dimensional signal as the vector input flow deserves further research. Bi-orthogonal wavelet has compact support, high-order vanishing moments and symmetry and its construction theory has attracted extensive attention and research from the people. This paper explores the applications of multi-wavelet in the image compression from the perspective of bi-orthogonal multi-wavelet and proposes the idea to use bi-orthogonal balanced multi-wavelet algorithm in the image compression. The result of the simulation experiment shows that to use this method in the image compression can obtain a higher peak signal to noise ratio and a relatively ideal compression ratio.
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.
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