We present a library of biorthogonal wavelet transforms and the related library of biorthogonal symmetric waveforms. For the construction we use interpolatory, quasiinterpolatory and smoothing splines with finite mask...
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ISBN:
(纸本)0819441929
We present a library of biorthogonal wavelet transforms and the related library of biorthogonal symmetric waveforms. For the construction we use interpolatory, quasiinterpolatory and smoothing splines with finite masks (local splines). With this base we designed a set of perfect reconstruction infinite and finite impulse response filter banks with linear phase property. The construction is performed in a "lifting" manner. The developed technique allows to construct wavelet transforms with arbitrary prescribed properties such as the number of vanishing moments, shape of wavelets, and frequency resolution. Moreover, the transforms contain some scalar control parameters which enable their flexible tuning in either time or frequency domains. The transforms are implemented in a fast way. The transforms, which are based on interpolatory splines, are implemented through recursive filtering. We present encouraging results towards image compression.
Modern medical imaging modalities produce increasingly large datasets. This trend can be in contrast with the computation and transmission time requirements coming from critical teleradiology applications. Multidimens...
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ISBN:
(纸本)9781457713033
Modern medical imaging modalities produce increasingly large datasets. This trend can be in contrast with the computation and transmission time requirements coming from critical teleradiology applications. Multidimensional image compression techniques can be considered as enabling solutions on condition that they are able to guarantee a suitable combination of rate- distortion and computational performance which fulfill all the application domain requirements. In this work, we present a parallel version of our 3D Embedded Morphological Dilation Coding algorithm that allows a significant reduction of computation costs and the concurrent conservation of coding performance and of other relevant bitstream properties. A comparison with the recently released JPEG2000 part 10 (JP3D) standard put in evidence the value of the proposed solution, especially for teleradiology applications over heterogeneous networks.
In digital histopathology, color standardization, known as stain normalization, is widely used in computer-aided diagnosis (CAD) systems. This study details the adaptation and implementation of the wavelet Knowledge D...
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ISBN:
(纸本)9798350388978;9798350388961
In digital histopathology, color standardization, known as stain normalization, is widely used in computer-aided diagnosis (CAD) systems. This study details the adaptation and implementation of the wavelet Knowledge Distillation (WKD) method to CAD systems. The proposed method focuses on knowledge transfer between the teacher and student models within a specially designed Pix2Pix Generative Adversarial Network (GAN) for stain normalization in histopathology images. The student model, guided by the knowledge transferred by the teacher model using wavelet-based feature extraction, significantly improves the accuracy of stain normalization, which is crucial for preserving histological details and image quality. The WKD method has demonstrated high performance on the publicly available paired MITOS-ATYPIA dataset, outperforming state-of-the-art methods. Using the same settings, the Teacher model achieved a PSNR of 25.559, SSIM of 0.934, and RMSE of 7.270. Additionally, the student model used in the method yielded better results in the Frechet Inception *** (FID) metric compared to the teacher and baseline models.
Functional (time-dependent) Magnetic Resonance Imaging can be used to determine which parts of the brain are active during various limited activities;these parts of the brain are called activation regions. In this pre...
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ISBN:
(纸本)0819450804
Functional (time-dependent) Magnetic Resonance Imaging can be used to determine which parts of the brain are active during various limited activities;these parts of the brain are called activation regions. In this preliminary study we describe some experiments that are suggested, from the following questions: Does one get improved results by analyzing the complex image data rather than just the real magnitude image data? Does wavelet shrinkage smoothing improve images? Should one smooth in time as well as within and between slices? If so, how should one model the relationship between time smoothness (or correlations) and spatial smoothness (or correlations). The measured data is really the Fourier coefficients of the complex image-should we remove noise in the Fourier domain before computing the complex images? In this preliminary study we describe some experiments related to these questions.
The key element in the design of fast algorithms in numerical analysis and signalprocessing is the selection of an efficient approximation for the functions and operators involved. In this talk we will consider appro...
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ISBN:
(纸本)0819441929
The key element in the design of fast algorithms in numerical analysis and signalprocessing is the selection of an efficient approximation for the functions and operators involved. In this talk we will consider approximations using wavelet and multiwavelet bases as well as a new type of approximation for bandlimited functions using exponentials obtained via Generalized Gaussian quadratures. Analytically, the latter approximation corresponds to using the basis of the Prolate Spheroidal Wave functions. We will briefly comment on the future development of approximation techniques and the corresponding fast algorithms.
Content-based image retrieval (CBIR) technique retrieves relevant images based on extracted features from image contents. Latent semantic indexing (LSI) is used as a semantic model in the CBIR field. This paper invest...
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ISBN:
(纸本)9781479964635
Content-based image retrieval (CBIR) technique retrieves relevant images based on extracted features from image contents. Latent semantic indexing (LSI) is used as a semantic model in the CBIR field. This paper investigates the capability of LSI-based CBIR in dealing with different types of image noise, and the impact of noise on the retrieval results. To construct the feature-image matrix (FIM) in the proposed LSI framework, three wavelet-based methods are used to extract texture feature: Gabor wavelet, Daubechies wavelet, and wavelet moments. The performance of the proposed system is evaluated by a predefined accuracy measure. The results show that the LSI-based CBIR achieves a high level of accuracy with the original image database, and still performs very well in dealing with different types of image noise.
It is well known that the image compression task can be effectively accomplished by means of the wavelet transform. A new method has been recently proposed for the computation of this transform, i.e. the Lifting Schem...
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ISBN:
(纸本)0819437646
It is well known that the image compression task can be effectively accomplished by means of the wavelet transform. A new method has been recently proposed for the computation of this transform, i.e. the Lifting Scheme (LS). Besides being computationally more efficient than the classical filter bank scheme, the LS also enables the computation of a wavelet transform which maps integers to integers, so allowing for the design of joint lossless and lossy compression schemes. The performance of this Integer wavelet Transform (IWT) has already been studied in the literature, and compared to that of the Discrete wavelet Transform (DWT) in the lossy case;it has been found that in most cases the DWT achieves slightly better performance with respect to the DWT. In this paper we show that this result does not hold in the case of lossy compression of smooth images, as the IWT has a much larger loss of performance, making it ineffective for the compression of such images. We first select measures of image smoothness;then we study the IWT lossy compression performance in the presence of different degrees of smoothness, on an set containing various kinds of images. Finally, we relate the IWT compression performance to the degree of image smoothness.
Multiscale statistical signal and image models resulted in major advances in many signalprocessing disciplines. This paper focuses on Bayesian image denoising. We discuss two important problems in specifying priors f...
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ISBN:
(纸本)0819451541
Multiscale statistical signal and image models resulted in major advances in many signalprocessing disciplines. This paper focuses on Bayesian image denoising. We discuss two important problems in specifying priors for imagewavelet coefficients. The first problem is the characterization of the marginal subband statistics. Different existing models include highly kurtotic heavy-tailed distributions, Gaussian scale mixture models and weighted sums of two different distributions. We discuss the choice of a particular prior and give some new insights in this problem. The second problem that we address is statistical modelling of inter- and intrascale dependencies between imagewavelet coefficients. Here we discuss the use of Hidden Markov Tree models, which are efficient in capturing inter-scale dependencies, as well as the use of Markov Random Field models, which are more efficient when it comes to spatial (intrascale) correlations. Apart from these relatively complex models, we review within a new unifying framework a class of low-complexity locally adaptive methods, which encounter the coefficient dependencies via local spatial activity indicators.
In this paper, we present a 3-D wavelet compression technique for image sequences in video conferencing applications. One of the main requirements for such applications is that the delay has to be within some acceptab...
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In this paper, we present a 3-D wavelet compression technique for image sequences in video conferencing applications. One of the main requirements for such applications is that the delay has to be within some acceptable limit. When applying wavelet decomposition in the temporal direction, we must store a large number of frames so that such a decomposition can be effective. But this translates into a correspondingly large algorithmic delay. A technique to avoid this obstacle will be proposed. The basic idea is to overlap the decomposition with previous frames that have already been transmitted and selectively transmitting only the new wavelet coefficients.
作者:
Schwarz, GDatcu, MDLR
German Remote Sensing Data Ctr German Aerosp Res Estab DFD D-82234 Oberpfaffenhofen Germany
During the last years, wavelets have become very popular in the fields of signalprocessing and pattern recognition and have led to a large number of publications. In the discipline of remote sensing several applicati...
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ISBN:
(纸本)0819426490
During the last years, wavelets have become very popular in the fields of signalprocessing and pattern recognition and have led to a large number of publications. In the discipline of remote sensing several applications of wavelets have emerged, too. Among them are such diverse topics as image data compression, image enhancement, feature extraction, and detailed data analysis. On the other hand, the processing of remote sensing image data-both for optical and radar data-follows a well-known systematic sequence of correction and data management steps supplemented by dedicated image enhancement and data analysis activities. In the following we will demonstrate where wavelets and wavelet transformed data can be used advantageously within the standard processing chain usually applied to remote sensing image data. Summarizing potential waveletapplications for remote sensing image data, we conclude that wavelets offer a variety of new perspectives especially for image coding, analysis, classification, archiving, and enhancement. However, applications requiring geometrical corrections and separate dedicated representation bases will probably remain a stronghold of classical image domain processing techniques.
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