We presents results obtained by different contrast enhancement methods applied to medical images. We take into account classical histogram specification, local and wavelet-based techniques and a novel approach for mul...
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ISBN:
(纸本)0818679204
We presents results obtained by different contrast enhancement methods applied to medical images. We take into account classical histogram specification, local and wavelet-based techniques and a novel approach for multiscale contrast enhancement. The latter, whose rationale grounds in theories of visual perception, exploits a local definition of the Fechner-Weber's contrast within the-context of a non-linear scale-space representation generated by anisotropic diffusion. Our experimental fields concerns a difficult kind of medical images, namely digital mammographic images.
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 i...
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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.
Malvar wavelets or lapped orthogonal transform has been recognized as a useful tool in eliminating block effects in transform coding. Suter and Oxley extended the Malvar wavelets to more general forms, which enable on...
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ISBN:
(纸本)0819416274;9780819416278
Malvar wavelets or lapped orthogonal transform has been recognized as a useful tool in eliminating block effects in transform coding. Suter and Oxley extended the Malvar wavelets to more general forms, which enable one to construct an arbitrary orthonormal basis on different intervals. In this paper, we generalize the idea in Suter and Oxley from 1D to 2D cases and construct nonseparable Malvar wavelets, which is potentially important in multidimensional signal analysis. With nonseparable Malvar wavelets, we then construct nonseparable Lemarie-Meyer wavelets which are band-limited.
In this paper, we propose an original decomposition scheme based on Meyer's wavelets. In opposition to a classical technique of wavelet packet analysis, the decomposition is an adaptative segmentation of the frequ...
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ISBN:
(纸本)0819432997
In this paper, we propose an original decomposition scheme based on Meyer's wavelets. In opposition to a classical technique of wavelet packet analysis, the decomposition is an adaptative segmentation of the frequential axis which does not use a filters bank. This permits a higher flexibility in the band frequency definition. The decomposition computes all possible partitions from a sequential space: it does not only compute those that come from a dyadic decomposition. Our technique is applied on the electroencephalogram signal (EEG);here the purpose is to extract a best basis of frequential decomposition. This study Is part of a multimodal functional cerebral imagery project.
We present a highly powerful, modular, and interactive software tool for the analysis of timefrequency coherent signals via wavelet transformations. A major design goal of the waveletsignalprocessing Workstation (WS...
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Despite their success in other areas of statistical signalprocessing, current wavelet-based image models are inadequate for modeling patterns in images, due to the presence of unknown transformations inherent in most...
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ISBN:
(纸本)0780362985
Despite their success in other areas of statistical signalprocessing, current wavelet-based image models are inadequate for modeling patterns in images, due to the presence of unknown transformations inherent in most pattern observations. In this paper we introduce a hierarchical wavelet-based framework for modeling patterns in digital images. This framework takes advantage of the efficient image representations afforded by wavelets, while accounting for unknown pattern transformations. Given a trained model, we can use this framework to synthesize pattern observations. If the model parameters are unknown, we can infer them from labeled training data using TEMPLAR (Template Learning from Atomic Representations), a novel template learning algorithm with linear complexity. TEMPLAR employs minimum description length (MDL) complexity regularization to learn a template with a sparse representation in the wavelet domain. We illustrate template learning with examples, and discuss how TEMPLAR applies to pattern classification and denoising from multiple, unaligned observations.
In this paper, the wavelet transform is used for the purpose of noise reduction and signal enhancement in order to aid in the detection of randomly occurring short duration signals in noisy environments with signal to...
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ISBN:
(纸本)0819411973
In this paper, the wavelet transform is used for the purpose of noise reduction and signal enhancement in order to aid in the detection of randomly occurring short duration signals in noisy environments with signal to noise ratios of about -30 dB. The noise is characterized as being additive and consists of correlated interference as well as Gaussian noise. Such problems are encountered in many applications, such as health diagnostics (e.g. electrocardiograms, echo-cardiograms and electroencephalograms), underwater acoustics and geophysical applications where a signature signal passes through multiple media. The wavelet transform, with its basis functions localized both in time and frequency, provides the user with a signal representation suitable for detection purposes. Following the introduction, a brief description of the problem with the characteristics of the signal to be detected and the noise that is present in the environment is given. Then, background information on the wavelet transform is presented. Finally, our results obtained by applying the wavelet transform to signal detection are shown.
The traditional mean-squared-error (MSE) or peak-signal-to-noise-ratio (PSNR) error measures are mainly focused on the pixel-by-pixel difference between the original and compressed images. Such metrics are improper fo...
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ISBN:
(纸本)0819425915
The traditional mean-squared-error (MSE) or peak-signal-to-noise-ratio (PSNR) error measures are mainly focused on the pixel-by-pixel difference between the original and compressed images. Such metrics are improper for subjective quality or fidelity assessment, since human perception is very sensitive to correlations between adjacent pixels. In this work, we explore the Haar wavelet to model the space-frequency localization property of human visual system (HVS). It is shown that the physical contrast in different resolutions can be easily represented in terms of transform coefficients. We model HVS with the Haar filter with several Visual mechanisms and develop a subjective quality measure which is more consistent with human observation experience.
Two-dimensional wavelet analysis with directional frames is well-adapted and efficient, for detecting oriented features in images. but, for (quasi-) isotropic components, it is unnecessarily redundant. Using wavelets ...
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ISBN:
(纸本)0819450804
Two-dimensional wavelet analysis with directional frames is well-adapted and efficient, for detecting oriented features in images. but, for (quasi-) isotropic components, it is unnecessarily redundant. Using wavelets with variable angular selectivity leads to a prohibitive computing cost in the continuous wavelet formalism. We propose here a solution based on a multiresolution analysis in the angular variable (transferred from a biorthogonal analysis on the line), in Addition to the usual multiresolution in scale. The resulting scheme is efficient and competitive with traditional methods. Some applications are given to image denoising.
image segmentation aims at partitioning an image into its constituent parts, which plays a crucial role in practical applications. In this paper, we present a wavelet frame-based model for color images segmentation, w...
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ISBN:
(纸本)9781728136608
image segmentation aims at partitioning an image into its constituent parts, which plays a crucial role in practical applications. In this paper, we present a wavelet frame-based model for color images segmentation, which can be regarded as a discretization to the classical Chan-Vese (C-V) model. The advantage of the wavelet frame-based approach is that it has fast algorithm and is able to extract important features of the input images. We then apply the alternating direction method of multipliers (ADAM) algorithm to solve the model. The experiments on some color image segmentation tasks indicate that our algorithm performs favorably against several existing methods.
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