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检索条件"任意字段=Wavelets: Applications in Signal and Image Processing X"

共 8037 条记录，以下是4971-4980 订阅

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image decomposition: Separation of texture from piecewise smooth content

Image decomposition: Separation of texture from piecewise sm...

引用

wavelets - applications in signal and image processing x Conference

作者： Starck, JL Elad, M Donoho, DL CEA Saclay DAPNIA SEDI SAP F-91191 Gif Sur Yvette France

ISBN: (纸本)0819450804

This paper presents a novel method for separating images into texture and piecewise smooth parts. The proposed approach is based on a combination of the Basis Pursuit Denoising (BPDN) algorithm and the Total-Variation (TV) regularization scheme. The basic idea promoted in this paper is the use of two appropriate dictionaries, one for the representation of textures, and the other for the natural scene parts. Each dictionary is designed for sparse representation of a particular type of image-content (either texture or piecewise smooth). The use of BPDN with the two augmented dictionaries leads to the desired separation, along with noise removal as a by-product. As the need to choose a proper dictionary for natural scene is very hard, a TV regularization is employed to better direct the separation process. Experimental results validate the algorithm's performance.

关键词： basis pursuit denoising total variation sparse representations piecewise smooth texture wavelet local DCT ridgelet curvelet

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Wavelet-based adaptive optics

Wavelet-based adaptive optics

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wavelets - applications in signal and image processing x Conference

作者： Jones, KJ Rice Univ Dept Math Houston TX 77005 USA

ISBN: (纸本)0819450804

Adaptive systems are useful when the signals or images axe changing with time. For example, with adaptive wavelets, different filters are used for different parts of the signal: the signal itself should indicate whether a high or low order filter should be used. With adaptive optics, rapidly varying atmospheric wavefront distortions in a medium changing with time is reduced using optics : ie. in astronomical adaptive optical systems, a system of control-driven deformable mirrors eliminates distortion produced by a medium changing with time. Adaptive wavelets has the potential for achieving the same objective while reducing cost. Adaptive optics provides real-time compensation for aberrations produced by atmospheric turbulence, jitter, and the optics. The adaptive optics subsystem consists of a Wavefront Sensor, Real-Time Reconstructor and Server Compensator, Deformable Mirror, Tilt Correction, Optical Assembly, and Adaptive Optics Control. The Wavefront Sensor senses phase difference and wavefront tilts. The Real-Time Reconstructor and Servo Compensation system computes the-Deformable Mirror actuator. The Tilt Correction system corrects wavefront tilt errors and angle of arrival jitter caused by atmospheric turbulence, mount vibration, wobble dynamics lag and system vibration. In summation, adaptive optics systems are highly complex and both assembly and maintenance very expensive. Adaptive wavelets offers the potential of simplifying the system and reducing the cost. The ultimate goal is higher image resolution. Adaptive systems axe important when the signals or environments are changing in time. With adaptive lifting, the prediction/update filters or wavelet/scaling functions are chosen in a fixed fashion. They can be chosen in such a way that a signal is approximated with very high accuracy using only a limited number of coefficients. Different prediction filters can be used for different parts of the signal. A high or low order prediction filter is chosen based

关键词： Adaptive optics Wavefronts wavelets Atmospheric optics Wavefront distortions Deformable mirrors Digital filtering Distortion Electronic filtering Atmospheric turbulence

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On the lifting construction of a class of non-separable 2D orthonormal wavelets 3

On the lifting construction of a class of non-separable 2D o...

引用

3rd International Symposium on image and signal processing and Analysis, ISPA 2003

作者： Zhan, Yinwei Heijmans, H.J.A.M. Centre for Mathematics and Computer Science Kruislaan 413 Amsterdam1098 SJ Netherlands

ISBN: (纸本)953184061X

In most cases 2D (or bivariate) wavelets are constructed as a tensor product of 1D wavelets. Such wavelets are called separable. However, there are various applications, e.g. in image processing, for which non-separable 2D wavelets are preferable. In this paper, we are concerned with the class of compactly supported 2D wavelets that was introduced by Belogay and Wang (1999). A characteristic feature of this class of wavelets is that the support of the corresponding filter comprises only two rows. As a result, the 2D wavelets in this class are intimately related to some underlying 1D wavelet. We explore this relation in detail, and we explain how the 2D decompositions can be realized by a lifting scheme, and hence allow an efficient implementation. We also describe an easy way to construct wavelets with more rows and shorter columns. © 2003 IEEE.

关键词： Tensors

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TBB (true best base) searching method and its applications

TBB (true best base) searching method and its applications

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wavelets - applications in signal and image processing x Conference

作者： Chen, HW Olson, T Lockheed Martin Missiles & Fire Control Orlando Orlando FL 32819 USA

ISBN: (纸本)0819450804

The binary-tree best base (BTBB) searching method developed by Coifman and Wickerhauser is well known and widely used in wavelet packet applications. However, the requirement that the base vectors be chosen from either a parent or its directly related children in the binary-tree structure is a limitation because it doesn't search all possible orthogonal bases and therefore may not provide a optimal result. We have recently found that the set of all possible orthogonal bases in a wavelet packet is much larger than the set searched by the BTBB method.. Based on this observation, we have developed the true best base (TBB) searching method - a new way to search the best base among a much larger set of orthogonal bases. In this paper, we show that considerable improvements in signal compression, de-noising, and time-frequency analysis can be achieved using the new TBB method. Furthermore, we show that the TBB method can be used as a searching engine to extract the local discriminant base (LDB) for feature extraction and signal/object classification, and we compare the performances of the LDBs extracted by the TBB and BTBB.

关键词： wavelets Associative arrays Feature extraction signal to noise ratio Discrete wavelet transforms Electronic filtering Time-frequency analysis Interference (communication) Wavelet packet decomposition Linear filtering

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Multiwavelet-like bases for high quality image interpolation

Multiwavelet-like bases for high quality image interpolation

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wavelets - applications in signal and image processing x Conference

作者： Ichige, K Blu, T Unser, M Yokohama Natl Univ Dept Elect & Comp Engn Yokohama Kanagawa 2408501 Japan

ISBN: (纸本)0819450804

We present a simple but generalized interpolation method for digital images that uses multiwavelet-like basis functions. Most of interpolation methods uses only one symmetric basis function;for example, standard and shifted piecewise-linear interpolations use the "hat" function only. The proposed method uses q different multiwavelet-like basis functions. The basis functions can be dissymmetric but should preserve the "partition of unity" property for high-quality signal interpolation. The scheme of decomposition and reconstruction of signals by the proposed basis functions can be implemented in a filterbank form using separable IIR implementation. An important property of the proposed scheme is that the prefilters for decomposition can be implemented by FIR filters. Recall that the shifted-linear interpolation requires IIR prefiltering, but we find a new configuration which reaches almost the same quality with the shifted-linear interpolation, while requiring FIR prefiltering only. Moreover, the present basis functions can be explicitly formulated in time-domain, although most of (multi-)wavelets don't have a time-domain formula. We specify an optimum configuration of interpolation parameters for image interpolation, and validate the proposed method by computing PSNR of the difference between multi-rotated images and their original version.

关键词： interpolation linear interpolation filterbank wavelet

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Features selection for clustering of fMRI data

Features selection for clustering of fMRI data

引用

wavelets - applications in signal and image processing x Conference

作者： Meyer, FG Chinrungrueng, J Univ Colorado Dept Elect Engn Boulder CO 80309 USA

ISBN: (纸本)0819450804

We address the problem of the analysis of event-related functional Magnetic Resonance images (fMRI). We propose to separate the fMRI time series into "activated" and "non-activated" clusters. The fMRI time series are projected onto a basis, and the clustering is performed using the coefficients in that basis. We developed a new algorithm to select that basis which provides the optimal clustering of the time series. Our approach does not require any training datasets or any model of the hemodynamic response. The basis is constructed using a dictionary of wavelet packets. We search for the optimal basis in this dictionary using a new cost function that measures the clustering power of a set of wavelet packets. Our approach can be easily extended to classification problems. We have conducted several experiments with synthetic and in-vivo event-related fMRI data. Our method is capable of discovering the structures of the synthetic data. The method also successfully detected activated voxels in the in-vivo fMRI.

关键词： wavelets Functional magnetic resonance imaging Hemodynamics signal to noise ratio Associative arrays Brain In vivo imaging Data modeling Algorithm development Feature selection

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Adaptive wavelet thresholding for multichannel signal estimation

Adaptive wavelet thresholding for multichannel signal estima...

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wavelets - applications in signal and image processing x Conference

作者： Atkinson, I Kamalabadi, F Jones, DL Do, MN Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

ISBN: (纸本)0819450804

In this paper, we illustrate how a recently proposed wavelet-based estimation scheme for 2-D multichannel signals can utilize an overcomplete wavelet expansion or the BayesShrink adaptive wavelet-domain threshold to improve estimation results. The existing technique approximates the optimal estimator using a DFT and an orthonormal 2-D DWT to efficiently decorrelate the signal in both channel and space, and a wavelet-domain threshold to suppress the noise. Although this technique typically yields signal-to-noise ratio (SNR) gains of over 12 dB, results can be improved 1 to 1.5 dB by replacing the critically-sampled wavelet expansion with an overcomplete wavelet expansion. In addition, provided that the detail subbands of the original signal channels each obey a generalized Gaussian distribution, average channel SNR gains can be improved 3 dB or more using the BayesShrink adaptive wavelet-domain threshold.

关键词： adaptive estimation multichannel overcomplete wavelet

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Improved bearing estimation in ocean using wavelet denoising

Improved bearing estimation in ocean using wavelet denoising

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wavelets - applications in signal and image processing x Conference

作者： Sathish, R Anand, GV Indian Inst Sci ECE Dept Bangalore 560012 Karnataka India

ISBN: (纸本)0819450804

Passive localisation and bearing estimation of underwater acoustic sources is a problem of great interest in the area of ocean acoustics. Bearing estimation techniques often perform poorly due to the low signal-to-noise ratio (SNR) at the sensor array. This paper proposes signal enhancement by wavelet denoising to improve the performance of the bearing estimation techniques. Methods have been developed in the recent past to effectively perform wavelet denoising in the multisensor scenario (wavelet array denoising). Following one such approach, the acoustic signal received at the array is spatially decorrelated and then denoised. The denoised and recorrelated signal is then used for bearing estimation employing known bearing estimation techniques (MUSIC and Subspace Intersection). It is shown that wavelet array denoising improves the performance of the bearing estimators significantly. Also the case of perturbed arrays is considered as a special case for application of wavelet array denoising. Simulation results show that the denoising estimator has lower mean square error and higher resolution.

关键词： bearing estimation array processing wavelet array denoising MUSIC subspace intersection

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Novel signal shape descriptors through wavelet transforms and dimensionality reduction

Novel signal shape descriptors through wavelet transforms an...

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wavelets - applications in signal and image processing x Conference

作者： Hughes, NP Tarassenko, L Univ Oxford Dept Engn Sci Oxford OX1 3PJ England

ISBN: (纸本)0819450804

The wavelet transform is a powerful tool for capturing the joint time-frequency characteristics of a signal. However, the resulting wavelet coefficients are typically high-dimensional, since at each time sample the wavelet transform is evaluated at a number of distinct scales. Unfortunately, modelling these coefficients can be problematic because of the large number of parameters needed to capture the dependencies between different scales. In this paper we investigate the use of algorithms from the field of dimensionality reduction to extract informative and compact descriptions of shape from wavelet coefficients. These low-dimensional shape descriptors lead to models that are governed by only a small number of parameters and can be learnt successfully from limited amounts of data. The validity of our approach is demonstrated on the task of automatically segmenting an electrocardiogram signal into its constituent waveform features.

关键词： undecimated wavelet transform PCA sammon's mapping LLE ECG interval analysis

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Application of wavelets in blind source separation

Application of wavelets in blind source separation

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wavelets - applications in signal and image processing x Conference

作者： Kisilev, P Zibulevsky, M Zeevi, YY Technion Dept Elect Engn IL-32000 Haifa Israel

ISBN: (纸本)0819450804

It was previously shown that sparse representations can improve. and simplify the estimation of an unknown mixing matrix of a set of images and thereby improve the quality of separation of source images. Here we propose a multiscale approach to the problem of blind separation of images from a set of their mixtures.' We take advantage of the properties of multiscale transforms such as wavelet packets and decompose signals and images according to sets of local features. The resulting partial representations on a tree of data structure depict various degrees of sparsity. We show how the separation error is affected by the sparsity of the decomposition coefficients, and by the misfit between the prior, formulated in accordance with the probabilistic model of the coefficients' distribution, and the actual distribution of the coefficients. Our error estimator, based on the Taylor expansion of the quasi Log-Likelihood function, is used in selection of the best subsets of coefficients, utilized in turn for further separation. The performance of the proposed method is assessed by separation of noise-free and noisy data. Experiments with simulated and real signals and images demonstrate significant improvement of separation quality over previously reported results.

关键词： wavelets Error analysis signal to noise ratio Independent component analysis Transform theory Associative arrays Chemical elements Computer simulations Optimization (mathematics) Sensors

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