On the base of local criteria of processing quality, a class of local adaptive linear filters for image restoration and enhancement is introduced. The filters work in a running window in the domain of DFT of DCT and h...
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ISBN:
(纸本)0819422134
On the base of local criteria of processing quality, a class of local adaptive linear filters for image restoration and enhancement is introduced. The filters work in a running window in the domain of DFT of DCT and have O (size of the window) computational complexity thanks to recursive algorithms of running DFT and DCT. The filter design and the recursive computation of running DCT are outlined and filtering for edge preserved noise suppression, blind image restoration and enhancement is demonstrated.
We investigate three approaches to VLSI implementation of wavelet filters. The direct form structure, the lattice form structure, and an algebraic structure are used to derive different architectures for wavelet filte...
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ISBN:
(纸本)0819422134
We investigate three approaches to VLSI implementation of wavelet filters. The direct form structure, the lattice form structure, and an algebraic structure are used to derive different architectures for wavelet filters. The algebraic structure exploits conjugacy properties in number fields. All approaches are explained in detail for the Daubechies 4- tab filters. We outline the philosophy of a design method for integrated circuits.
wavelet shrinkage is a signal estimation technique that exploits the remarkable abilities of the wavelet transform for signal compression. wavelet shrinkage using thresholding is asymptotically optimal in a minimax me...
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ISBN:
(纸本)0819425915
wavelet shrinkage is a signal estimation technique that exploits the remarkable abilities of the wavelet transform for signal compression. wavelet shrinkage using thresholding is asymptotically optimal in a minimax mean-square error (MSE) sense over a variety of smoothness spaces. However, for any given signal, the MSE-optimal processing is achieved by the Wiener filter, which delivers substantially improved performance. In this paper, we develop a new algorithm for wavelet denoising that uses a wavelet shrinkage estimate as a means to design a wavelet-domain Wiener filter. The shrinkage estimate indirectly yields an estimate of the signal subspace that is leveraged into the design of the filter. A peculiar aspect of the algorithm is its use of two wavelet bases: one for the design of the empirical Wiener filter and one for its application. Simulation results show up to a factor of 2 improvement in MSE over wavelet shrinkage, with a corresponding improvement in visual quality of the estimate. Simulations also yield a remarkable observation: whereas shrinkage estimates typically improve performance by trading bias for variance or vice versa, the proposed scheme typically decreases both bias and variance compared to wavelet shrinkage.
Conventional implementation of multi-dimensional wavelet transform (e.g. 3-D wavelet) requires whether a high amount of "in access" memory or a continual access to slow memory of a processor which makes it i...
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ISBN:
(纸本)0780376633
Conventional implementation of multi-dimensional wavelet transform (e.g. 3-D wavelet) requires whether a high amount of "in access" memory or a continual access to slow memory of a processor which makes it infeasible for most applications. In this paper, we proposed a novel algorithm for computation of an n-D discrete wavelet transform (DWT) based on lifting scheme. In addition to benefits of lifting scheme (which causes a major reduction in computational complexity and performs the total computations in time domain), our real-time approach computes the coefficients for all kinds of 1(st) and 2(nd) generation wavelets with short delay and optimized utilization of the slow and fast memories of a processor.
Volume data such as those acquired by magnetic resonance imaging techniques can be compressed efficiently using the wavelet transform. wavelet compression methods need to retain both the value and the location of the ...
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ISBN:
(纸本)0819422134
Volume data such as those acquired by magnetic resonance imaging techniques can be compressed efficiently using the wavelet transform. wavelet compression methods need to retain both the value and the location of the significant coefficients. We present experimental results demonstrating the use of zerotree encoding methods in wavelet compression can enhance the ability to further compress volume data.
Polarization in optical waveguides is always an aspect of optical loss and consequently impacts device performance. waveletimageprocessing allows a means to detect optical signals buried under noise. Orthogonality i...
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ISBN:
(纸本)0819445878
Polarization in optical waveguides is always an aspect of optical loss and consequently impacts device performance. waveletimageprocessing allows a means to detect optical signals buried under noise. Orthogonality is an essential element in wavelet bases. There are three primary types of multiresolution bases: orthogonal wavelet bases, semiorthogonal wavelet bases, and biorthogonal wavelet bases. waveletimageprocessing will be applied to laser beam propagation in lithium niobate and nonlinear polymer waveguides to achieve detection of signals below noise and a better understanding of polarization as an aspect of device performance.
First, a general procedure for constructing a variety of orthogonal wavelets which are compactly supported in frequency domain is presented. These orthogonal wavelets, as a result of the method of Multiresolution Anal...
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ISBN:
(纸本)0780336798;0780336801
First, a general procedure for constructing a variety of orthogonal wavelets which are compactly supported in frequency domain is presented. These orthogonal wavelets, as a result of the method of Multiresolution Analysis(MRA), share the same space structure. This property makes it possible to construct a ''combined'' orthonormal base of L-2(R) which contains more than one wavelet functions. This kind of combined orthonormal wavelet bases are more flexible and are expected to be more efficient than the conventional wavelet bases in data compression, speech coding and image coding.
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-noi...
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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.
Hidden Markov models have been used in a wide variety of wavelet-based statistical signalprocessingapplications. Typically, Gaussian mixture distributions are used to model the wavelet coefficients and the correlati...
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ISBN:
(纸本)0780370414
Hidden Markov models have been used in a wide variety of wavelet-based statistical signalprocessingapplications. Typically, Gaussian mixture distributions are used to model the wavelet coefficients and the correlation between the magnitudes of the wavelet coefficients within each scale and/or across the scales is captured by a Markov tree imposed on the (hidden) states of the mixture. This paper investigates correlations directly among the wavelet coefficient amplitudes (sign x magnitude), instead of magnitudes alone. Our theoretical analysis shows that the coefficients display significant correlations in sign as well as magnitude, especially near strong edges. We propose a new wavelet-based HMM structure based on mixtures of one-sided exponential densities that exploits both sign and magnitude correlations. We also investigate the application of this for denoising the signals corrupted by additive white Gaussian noise. Using some examples with standard test signals, we show that our new method can achieve better mean squared error, and the resulting denoised signals are generally much smoother.
wavelet analysis and its application has found much attention in recent times. It is vastly applied in many applications such as involving transient signal analysis, imageprocessing, signalprocessing and data compre...
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ISBN:
(纸本)9780819489326
wavelet analysis and its application has found much attention in recent times. It is vastly applied in many applications such as involving transient signal analysis, imageprocessing, signalprocessing and data compression. It has gained popularity because of its multiresolution, subband coding and feature extraction features. The paper describes efficient application of wavelet analysis for image compression, exploring Daubechies wavelet as the basis function. wavelets have scaling properties. They are localized in time and frequency. wavelets separate the image into different scales on the basis of frequency content. The resulting compressed image can then be easily stored or transmitted saving crucial communication bandwidth. wavelet analysis because of its high quality compression is one of the feature blocks in the new JPEG2000 image compression standard. The paper proposes Daubechies wavelet analysis, quantization and Huffman encoding scheme which results in high compression and good quality reconstruction.
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