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wavelets - applications in signal and image processing x Conference

作者： Böhme, M Potts, D Univ Lubeck D-23560 Lubeck Germany

ISBN: (纸本)0819450804

Spherical filters have recently been introduced in order to avoid the spherical harmonic transform. Spherical filtering can be used in a variety of applications, such as climate modelling, electromagnetic and acoustic scattering, and several other areas. However, up to now these methods have been restricted to special grids on the sphere. The main reason for this was to enable the use of FFT techniques. In this paper we extend the spherical filter to arbitrary grids by using the the Nonequispaced Fast Fourier Transform (NFFT).(1) The new algorithm can be applied to a variety of different distributions on the sphere, equidistributions on the sphere being an important example. The algorithm's performance is illustrated with several numerical examples.

关键词： spherical filter spherical Fourier transform spherical harmonics associated Legendre functions fast discrete transforms fast Fourier transform at nonequispaced knots wavelets fast discrete summation spherical polynomials

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Source adaptive blind source separation: Gaussian models and sparsity

Source adaptive blind source separation: Gaussian models and...

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wavelets - applications in signal and image processing x Conference

作者： Pham, DT Cardoso, JF Lab Modelisat & Calcul F-38041 Grenoble 9 France

ISBN: (纸本)0819450804

Using the time-frequency (or -scale) diversity of the source processes allows the blind source separation problem to be tackled within Gaussian models. In this work, we show that this approach amounts to minimizing a certain sparseness criterion for the energy distribution of the source over the time-frequency (or -scale) plane. We also explore the link between independence and sparsity and shows that other sparsity criteria (some examples of which are provided) can be used. Further, we introduce an adaptive method which tries to find the best sparse representation of the. source energy in order to exploit the sparsity in a most efficient way. An algorithm, adapted from that of Coifman and Wickerhauser has been developed for this end. Finally a simulation example has been given.

关键词： Chemical species Time-frequency analysis Algorithm development Error analysis Matrices Statistical analysis signal processing Statistical modeling Stochastic processes wavelets

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wavelets, curvelets and multiresolution analysis techniques in fast Z pinch research

Wavelets, curvelets and multiresolution analysis techniques ...

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wavelets - applications in signal and image processing x Conference

作者： Afeyan, B Won, K Starck, JL Cuneo, M Polymath Res Inc Pleasanton CA USA

ISBN: (纸本)0819450804

Z pinches produce an x ray rich plasma environment where backlighting imaging of imploding targets can be quite challenging to analyze. What is required is a detailed understanding of the implosion dynamics by studying snapshot images of its in flight deformations away from a spherical shell. We have used wavelets, curvelets and multiresolution analysis techniques to address some of these difficulties and to establish the Shell Thickness Averaged Radius (STAR) of maximum density, r*(N, theta), where N is the percentage of the shell thickness over which we average. The non-uniformities of r*(N, theta) are quantified by a Legendre polynomial decomposition in angle, theta, and the identification of its largest coefficients. Undecimated wavelet decompositions outperform decimated ones in denoising and both are surpassed by the curvelet transform. In each case, hard thresholding based on noise modeling is used.

关键词： wavelets curvelets multiresolution analysis implosion symmetry Legendre polynomial decompositions denoising hard thresholding noise modeling Z pinches

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applications of wavelets in morphometric analysis of medical images

Applications of wavelets in morphometric analysis of medical...

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wavelets - applications in signal and image processing x Conference

作者： Davatzikos, C Tao, xD Shen, DG Univ Penn Dept Radiol Sect Biomed Image Anal Philadelphia PA 19104 USA

ISBN: (纸本)0819450804

Morphometric analysis of medical images is playing an increasingly important role in understanding brain structure and function, as well as in understanding the way in which these change during development, aging and pathology. This paper presents three wavelet-based methods with related applications in morphometric analysis of magnetic resonance (MR) brain images. The first method handles cases where very limited datasets;are available for the training of statistical shape models in the deformable segmentation. The method is capable of capturing a larger range of shape variability than the standard active shape models (ASMs) can, by using the elegant spatial-frequency decomposition of the shape contours provided by wavelet transforms. The second method addresses the difficulty of finding correspondences in anatomical images, which is a key step in shape analysis and deformable registration. The detection of anatomical correspondences is completed by using wavelet-based attribute vectors as morphological signatures of voxels. The third method uses wavelets to characterize the morphological measurements obtained from all voxels in a brain image, and the entire set of wavelet coefficients is further used to build a brain classifier. Since the classification scheme operates in a very-high-dimensional space, it can determine subtle population differences with complex spatial patterns. Experimental results are provided to demonstrate the performance of the proposed methods.

关键词： wavelet transform morphological analysis active shape model attribute vector morphological classification

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Optimization of wavelet decomposition for image compression and feature preservation

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2003年第9期22卷 1141-1151页

作者： Lo, SCB Li, H Freedman, MT Georgetown Univ Med Ctr Dept Radiol Ctr Imaging Sci & Informat Syst Washington DC 20007 USA Georgetown Univ Med Ctr Dept Radiol ISIS Ctr Washington DC 20007 USA

A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet (whose low-pass filter coefficients are 0.32252136, 0.85258927, 0.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.

关键词： image compression image feature restoration neural network optimization of wavelet wavelet decomposition

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image registration using threefold orthogonal wavelet

Image registration using threefold orthogonal wavelet

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wavelets - applications in signal and image processing x Conference

作者： Gefen, S Tretiak, O Nissanov, J Drexel Univ Coll Med Comp Vis Lab Vertebrate Brain Mapping Dept Neurobiol & Anat Philadelphia PA 19129 USA

ISBN: (纸本)0819450804

An algorithm for multidimensional nonlinear registration is proposed. The deformation field between two elastic bodies is represented by a multi-resolution separable wavelet. Using a progressive approach that reduces algorithm complexity the registration parameters are recovered in a coarse to fine order. A custom wavelet that approximates threefold orthogonality is developed. The performance of the algorithm is evaluated by the alignment of sections from mouse brains. The wavelet registration algorithm demonstrated on average fourfold improvement in section alignment over linear alignment.

关键词： elastic deformation nonlinear registration threefold orthogonal wavelet segmentation mouse brain spatial normalization

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Separation of reflections via sparse ICA

Separation of reflections via sparse ICA

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wavelets - applications in signal and image processing x Conference

作者： Bronstein, AM Bronstein, MM Zibulevsky, M Zeevi, YY Technion Israel Inst Technol Dept Elect Engn IL-32000 Haifa Israel

ISBN: (纸本)0819450804

We consider the problem of recovery of a scene recorded through a semirefective medium from its mixture with a virtual reflected image using the: blind source separation (BSS) framework. We extend the Sparse ICA (SPICA) approach and apply it to the separation of the desired image from the superimposed images, without having any a priory knowledge about its structure and/or statistics. Advances in the SPICA approach are discussed. Simulations and experimental results illustrate the efficiency of the proposed approach, and of its specific implementation in a simple algorithm of a low computational cost. The approach and the algorithm are generic and can be adapted and applied to a wide range of BSS problems involving one-dimensional signals or images.

关键词： Independent component analysis Photography Polarizers Cameras image restoration Imaging systems Glasses Computer simulations image transmission Linear polarizers

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Unsupervised image segmentation using wavelet-domain hidden Markov models

Unsupervised image segmentation using wavelet-domain hidden ...

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wavelets - applications in signal and image processing x Conference

作者： Song, xM Fan, GL Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

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.

关键词： unsupervised segmentation wavelet hidden Markov models multiscale clustering

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Estimation error bounds for frame denoising

Estimation error bounds for frame denoising

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wavelets - applications in signal and image processing x Conference

作者： Fletcher, AK Ramchandran, K Univ Calif Berkeley Berkeley Audio Visual Signal Proc & Commun Syst G Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

ISBN: (纸本)0819450804

A subspace-based method for denoising with a frame works as follows: If a signal is known to have a sparse representation with respect to the frame, the signal can be estimated from a noise-corrupted observation of the signal by finding the best sparse approximation to the observation. The ability to remove noise in this manner depends on the frame being designed to efficiently represent the signal while it inefficiently represents the noise. This paper gives bounds to show how inefficiently white Gaussian noise is represented by sparse linear combinations of frame vectors. The bounds hold for any frame so they are generally loose for frames designed to represent structured signals. Nevertheless, the bounds can be combined with knowledge of the approximation efficiency of a given family of frames for a given signal class to study the merits of frame redundancy for denoising.

关键词： denoising Gaussian signals matching pursuit Occam filters rate-distortion subspace fitting thresholding

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Iterative projective wavelet methods for denoising

Iterative projective wavelet methods for denoising

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wavelets - applications in signal and image processing x Conference

作者： Fletcher, AK Goyal, VK Ramchandran, K Univ Calif Berkeley Berkeley Audio Visual Signal Proc & Commun Syst G Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

ISBN: (纸本)0819450804

Wavelet thresholding is a powerful tool for denoising images and other signals with sharp discontinuities. Using different wavelet bases gives different results, and since the wavelet transform is not time-invariant, thresholding various shifts of the signal is one way to use different wavelet bases. This paper describes several denoising methods that apply wavelet thresholding or variations on wavelet thresholding recursively. (We previously termed one of these methods "recursive cycle spinning.") These methods are compared experimentally for denoising piecewise polynomial signals. Though similar, the methods differ in computational complexity, convergence speed, and sensitivity to threshold selection.

关键词： adaptive projection cycle spinning estimation frames shift invariance thresholding

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