This paper develops new algorithms for adapted multiscale analysis and signal adaptive wavelet transforms. We construct our adaptive transforms with the lifting scheme, which decomposes the wavelet transform into pred...
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ISBN:
(纸本)0819432997
This paper develops new algorithms for adapted multiscale analysis and signal adaptive wavelet transforms. We construct our adaptive transforms with the lifting scheme, which decomposes the wavelet transform into prediction and update stages. We adapt the prediction stage to the signal structure and design the update stage to preserve the desirable properties of the wavelet transform. The resulting scale and spatially adaptive transforms are extended to the image estimation problem;our new image transforms show improved denoising performance over existing (non-adaptive) orthogonal transforms.
This paper discusses a method for estimating glottal flow derivative model parameters using the wavelet-smoothed excitation. The excitation is first estimated using the Weighted Recursive Least Squares with Variable F...
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ISBN:
(纸本)0780370414
This paper discusses a method for estimating glottal flow derivative model parameters using the wavelet-smoothed excitation. The excitation is first estimated using the Weighted Recursive Least Squares with Variable Forgetting Factor algorithm. The raw excitation is then smoothed by applying a Discrete wavelet Transform (DWT) using Biorthogonal Quadrature filters, and a thresholding operation done on the DWT amplitude coefficients, followed by an inverse DWT. The pitch period and the instant of glottal closure (IGC) are estimated from the wavelet-smoothed excitation. A six-parameter glottal flow derivative model consisting of three amplitude and three timing parameters is aligned with the IGC and optimized by minimum square error fitting to the speech waveform. The optimization is done by the method of simulated annealing The model is then used to reestimate the vocal-tract filter parameters in an ARX procedure followed by further stages of voice source-vocal tract estimation. The results of analysis of speech utterances from the BK_TIMIT database will be presented.
wavelet packet division multiplexing (WPDM) is a high-capacity, flexible and robust orthogonal multiplexing scheme in which the message signals are waveform coded onto wavelet packet basis functions for transmission. ...
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ISBN:
(纸本)0780370414
wavelet packet division multiplexing (WPDM) is a high-capacity, flexible and robust orthogonal multiplexing scheme in which the message signals are waveform coded onto wavelet packet basis functions for transmission. However, WPDM suffers from severe performance degradation in the presence of high-power amplifier (HPA) nonlinearities. In this paper, data predistortion using the pth-order Volterra inverse is proposed to combat the amplifier nonlinearities in a WPDM system. A 5th-order Volterra inverse with truncated memory length is designed based on the Volterra series channel model. Computer simulations are presented to demonstrate the capability of the proposed technique in compensating amplifier nonlinearities even under system parameter discrepancy. Guidelines are also proposed for designing wavelet filter which leads to better predistortion with the truncated Volterra inverse.
wavelet decomposition has recently been generalized to binary field in which the arithmetic is performed wholly in GF(2). In order to maintain an invertible binary wavelet transform with desirable multiresolution prop...
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ISBN:
(纸本)0780370414
wavelet decomposition has recently been generalized to binary field in which the arithmetic is performed wholly in GF(2). In order to maintain an invertible binary wavelet transform with desirable multiresolution properties, the bandwidth, the perfect reconstruction and the vanishing moment constraints are placed on the binary filters. While they guarantee an invertible transform, the transform becomes non-orthogonal and non-biorthogonal in which the inverse filters could be signal length-dependent. We propose to apply the perpendicular constraint on the binary filters to make them length independent. A filter design strategy is outlined in which a filter design for a length of eight is given. We also propose an efficient implementation structure for the binary filters that saves memory space and reduces the computational complexity.
Prediction of fine scale DWT coefficients from coarser scales is not generally possible. On the other hand, the redundancy of the shift-invariant Complex wavelet Transform (CWT) should allow good coarse-to-fine scale ...
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ISBN:
(纸本)0780362934
Prediction of fine scale DWT coefficients from coarser scales is not generally possible. On the other hand, the redundancy of the shift-invariant Complex wavelet Transform (CWT) should allow good coarse-to-fine scale prediction. The CWT's magnitude and phase step responses are well-behaved functions of a coefficient's normal distance to an edge. We present a coefficient prediction algorithm which assumes an edge-based image model. The algorithm infers information about local edges from coarse scale coefficients, and predicts the effect of these edges on the magnitudes and phases of finer level coefficients. Accurate coarse-to-fine prediction of coefficients has important potential benefits for image coding and resolution enhancement systems.
Nonlinearities are often encountered in the analysis and processing of real-world signals. This paper develops new signal decompositions for nonlinear analysis and processing. The theory of tensor norms is employed to...
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ISBN:
(纸本)0819422134
Nonlinearities are often encountered in the analysis and processing of real-world signals. This paper develops new signal decompositions for nonlinear analysis and processing. The theory of tensor norms is employed to show that wavelets provide an optimal basis for the nonlinear signal decompositions. The nonlinear signal decompositions are also applied to signalprocessing problems.
作者：
Starck, JLCEA
SEI SAP DAPNIA F-91191 Gif Sur Yvette France
We present in this paper a new way to measure the information in a signal, based on noise modeling. We show that the use of such an entropy-related measure leads to good results for signal restoration.
ISBN:
(纸本)0819432997
We present in this paper a new way to measure the information in a signal, based on noise modeling. We show that the use of such an entropy-related measure leads to good results for signal restoration.
In this paper, an adaptive separable 2D wavelet transform is proposed. wavelet transforms are widely used in signal and imageprocessing due to its energy compaction property. Sparser representation corresponds to bet...
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In this paper, an adaptive separable 2D wavelet transform is proposed. wavelet transforms are widely used in signal and imageprocessing due to its energy compaction property. Sparser representation corresponds to better performance in compression, denoising, compressive sensing, sparse component analysis and many other applications. The proposed scheme results in more compact representation then fixed wavelet. Instead of the commonly used least squares criterion, least absolute deviation (LAD) is introduced. It results in more accurate adaptation resistant to outliers. The advantages of the proposed method have been shown on synthetic and real-world images.
Our motivation for this research is twofold. Firstly, we Embed the watermark in wavelet domain rather than DCT domain motivated by the fact that the wavelet transform is used in jpeg2000 standardization process. Secon...
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ISBN:
(纸本)0780362934
Our motivation for this research is twofold. Firstly, we Embed the watermark in wavelet domain rather than DCT domain motivated by the fact that the wavelet transform is used in jpeg2000 standardization process. Secondly, high bit rate watermarks tend to get destroyed, even before between 10% to 32% average lossless compression. Hence we intend to get the watermark detection quality decreasing as we Increase the compression ratio to imp rove defection performance. We will do this by introducing the concept of multiresolution watermark channels. We will develop techniques to construct such watermark channels.
In this study, we aimed to determine whether the medical image belongs to that class or not, using the textural features of medical images. The study was performed on the images in IRMA (image Retrieval in Medical App...
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ISBN:
(纸本)9781467373869
In this study, we aimed to determine whether the medical image belongs to that class or not, using the textural features of medical images. The study was performed on the images in IRMA (image Retrieval in Medical applications), the international database. After performing pre process on the our current medical images, discrete wavelet transform (DWT) was applied and then discrete cosine transform (DCT) was applied to each band components. After feature extraction, using of 1%, 3%, 5% and 7% of the obtained data were classified. K-Nearest neighbor algorithm (KNN) was used in the classification phase. The classificaiton performance was around 87%.
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