The wavelet transform has gained much in popularity recently. Although the concepts underlying the wavelet transform have been used for some time, it is only in the last seven years that it began to have an impact, es...
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ISBN:
(纸本)0819419117
The wavelet transform has gained much in popularity recently. Although the concepts underlying the wavelet transform have been used for some time, it is only in the last seven years that it began to have an impact, especially on signal and imageprocessing. wavelets have applications in differential equations, signalprocessing, image and video compression, and many other domains. We provide a brief introduction to wavelets and wavelet analysis, and compare the wavelet and Fourier transforms. The wavelet transform allows us to analyze nonstationary signals, which the Fourier transform cannot. This is a very important property of wavelets. A wavelet decomposition makes it possible to analyze a signal both in time (or space) and frequency domains and is appropriate for multiresolution analysis. One interesting application of wavelets is image fusion. For this application we take the wavelet transform of images coming from different sensors (e.g., visible and infrared). This provides us with a multiresolution description of visible and infrared images. The two images are then merged at each level of resolution. Applying the inverse wavelet transform on the resulting image generates a new image which is a composite of the two original ones. This concept can be applied to more than two images whether they are in the same spectral band or not. Some results are presented and compared with the classical pyramidal algorithms of Burt and Toet.
We present a generalization of the scale substraction filter (SSF) of Gregoris et al. and apply it to the problem of filtering objects, within an image, by size.
ISBN:
(纸本)0819418447
We present a generalization of the scale substraction filter (SSF) of Gregoris et al. and apply it to the problem of filtering objects, within an image, by size.
One of the generic image/signalprocessing problems is how to restore accurately the first order point singularity feature having the discontinuity of the first derivative that is furthermore masked with nosy clutter ...
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ISBN:
(纸本)0819418447
One of the generic image/signalprocessing problems is how to restore accurately the first order point singularity feature having the discontinuity of the first derivative that is furthermore masked with nosy clutter environment. This is known as the class of ill-condition inverse problem in a real world environment. Specifically, given the noisy sensor data, how to reconstruct the unknown singularity as the feature of the unknown source/object. We consider the rooftop singularity for the noisy sensor inverse problem, and demonstrate that the multiresolution paradigm-wavelet transform is useful to restore the source/object singularity.
New approaches in the field of motion-compensated spatio-temporal filters applied to digital image sequences are presented. Motion-compensated filters are defined as temporal filters applied along assumed motion traje...
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ISBN:
(纸本)0780324323
New approaches in the field of motion-compensated spatio-temporal filters applied to digital image sequences are presented. Motion-compensated filters are defined as temporal filters applied along assumed motion trajectories. This paper deals with 3-D spatio-temporal filters and aims at generalizing the motion-compensated temporal filtering process as the product of two distinct operators. Multiresolution filters or wavelets may be consequently applied along the motion trajectories to produce optimum and adaptive resulting procedures for purposes like spatio-temporal prediction, interpolation and smoothing. applications are provided to cover the fields of image sequence coding and interpolation.
In recent years there has been a great deal of interest in the use of wavelets to supplement or replace conventional Fourier transform signalprocessing. This paper provides a review of wavelet transforms for signal p...
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ISBN:
(纸本)0819420131
In recent years there has been a great deal of interest in the use of wavelets to supplement or replace conventional Fourier transform signalprocessing. This paper provides a review of wavelet transforms for signalprocessingapplications, and discusses several emerging applications which benefit from the advantages of wavelets. The wavelet transform can be implemented as an acousto-optic correlator;perfect reconstruction of digital signals may also be achieved using acousto-optic finite impulse response filter banks. Acousto-optic image correlators are discussed as a potential implementation of the wavelet transform, since a 1D wavelet filter bank may be encoded as a 2D image. We discuss applications of the wavelet transform including nondestructive testing of materials, biomedical applications in the analysis of EEG signals, and interference excision in spread spectrum communication systems. Computer simulations and experimental results for these applications are also provided.
We used morphological filters to approximate wavelet scaling functions for multiresolution processing of an image. Because some spatial light modulators (SLMs) can only display binary data, waveletprocessing of binar...
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ISBN:
(纸本)081941834X
We used morphological filters to approximate wavelet scaling functions for multiresolution processing of an image. Because some spatial light modulators (SLMs) can only display binary data, waveletprocessing of binary images is inhibited. Therefore, we considered an alternative way - morphological processing - to generate a wavelet representation that consists entirely of binary elements. The effects of these filters are dependent on the input signal and cannot be generalized. Therefore, we used a statistical approach to approximate the scaling functions or various wavelets using morphological filters.
wavelet transform (WT) is concatenated with the modified Hadamard-structured discrete cosine transform (MHDCT) to encode imagesignals. As the outer code, the WT decomposes the imagesignals into uncorrelated low reso...
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wavelet transforms applied to multiresolution analyses of images produce outputs similar in theory to those of matched filters. In order to maximize the output at the location and scale of a signal of interest, it is ...
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ISBN:
(纸本)0819418447
wavelet transforms applied to multiresolution analyses of images produce outputs similar in theory to those of matched filters. In order to maximize the output at the location and scale of a signal of interest, it is necessary for the wavelet used in the multiresolution analysis to `match' the signal of interest. Current techniques match a signal to one of several predefined wavelets in a library, which requires wavelets to be designed in advance. Here, we present an alternative by developing a technique for deriving the wavelet directly from the desired signal spectrum in such a way that the mean squared error between their spectra is a minimum. Furthermore, the matched wavelet is designed such that its associated scaling function generates an orthonormal multiresolution analysis. The technique includes an algorithm for finding the scaling function from an orthonormal wavelet, and algorithms for finding the optimal wavelet magnitude and phase from a given input signal. Several examples are shown to demonstrate the performance of the technique for both known orthonormal wavelets and arbitrary signals.
In this paper, we compare for image coding applications a low-complexity iiR wavelet based on an allpass polyphase decomposition to a pair of linear phase biorthogonal wavelets. To code the wavelet coefficients, we us...
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In this paper, we compare for image coding applications a low-complexity iiR wavelet based on an allpass polyphase decomposition to a pair of linear phase biorthogonal wavelets. To code the wavelet coefficients, we use Shapiro's zerotree algorithm which has the virtues of being both efficient and delivering excellent performance (in a rate-distortion sense). We consider a variety of methods for eliminating filter transients at the image boundaries including circular convolution, symmetric extension (for the biorthogonal wavelets), and direct transmission (for the iiR wavelet). By also coding the filter states in a zerotree form, we find that direct transmission generally performs better than circular convolution. Finally, we show that the use of this iiR wavelet provides equivalent performance to the biorthogonal wavelets at greatly reduced computational complexity.
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