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image coding with wavelet software package using lattice quantization

Image coding with wavelet software package using lattice qua...

引用

Proceedings of the 1996 IEEE Region 10 TENCON - Digital signal processing applications conference. Part 2 (of 2)

作者： Shnaider, Mikhail Paplinski, Andrew P. Monash Univ Melbourne

An application of lattice quantization for encoding the wavelet coefficients of grey scale images is examined in this paper. The wavelet expansion is performed using the recently developed wavelet software package. In this paper we formally show that lattices give rise to optimal or near optimal quantizers for wavelet coefficients. The theoretical considerations are supported by a number of simulation experiments.

关键词： image coding

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wavelet filtering in the scale domain

Wavelet filtering in the scale domain

引用

4th conference on wavelet applications in signal and image processing

作者： Kaiser, G UNIV LOWELL DEPT MATH SCILOWELLMA 01854

ISBN: (纸本)0819422134

For a given basic wavelet (psi) (t), two distinct correspondences (called C1 and C2) are established between frequency filters, defined in the frequency domain through multiplication by a transfer function W(f), and scale filters, defined in the wavelet domain through multiplication by a scale transfer function w((sigma) ). W(f) is obtained by performing a scaling convolution of w((sigma) ) with (psi) (f)* (for C1) or its spectral energy density (psi) (f) ² (for C2). For a large class of transfer functions W(f), this relation can be solved for w((sigma) ) by applying the Mellin transform. We call such frequency filters and their associated time-domain convolution operators C1- or C2-admissible with respect to (psi) . In particular, the identity operator (W(f) equalsV 1) is C2-admissible if and only if the wavelet (psi) is admissible in the conventional sense. The implementation of the correspondence C1 is computationally simpler than C2, but C2 can be generalized to time-dependent filters. applications are proposed to the analysis of atmospheric turbulence data and wideband Doppler filtering.

关键词： wavelets filters transfer functions scale Mellin transform spectral smoothing wideband Doppler filtering

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Real-time embedded image processing applications using the A(3) methodology

Real-time embedded image processing applications using the A...

引用

International conference on image processing (ICIP-96)

作者： Sorel, Y INST NATL RECH INFORMAT & AUTOMAT F-78153 LE CHESNAYFRANCE

ISBN: (纸本)0780332598

In order to cope with the needs involved by real-time embedded image processing applications, multicomponent architectures are required. We used the well known Edge Detection algorithm to demonstrate how the A³ methodology (Algorithm Architecture Adequation), and the CAD software SynDEx which supports it, may improve the implementation of such algorithms on a multi-DSP architectures. The proposed approach, based on accurate algorithm and hardware architecture models, reduces significantly the development cycle of image processing applications, by simplifying test and debug process. Moreover, it allows to minimize the hardware resources as required for embedding, while satisfying real-time constraints.

关键词： image processing

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Multiresolution adaptive noise filtering based on Laplacian pyramids

Multiresolution adaptive noise filtering based on Laplacian ...

引用

4th conference on wavelet applications in signal and image processing

作者： Aiazzi, B Alparone, L Baronti, S Borri, G Susini, C CNR IROENELLO CARRARA RES INST ELECTROMAGNET WAVESI-50127 FLORENCEITALY

ISBN: (纸本)0819422134

A recently investigated approach to noise filtering in digital images consists of considering a multiresolution decomposition of the input image, and applying a different adaptive filter to each resolution layer. The wavelet decomposition has been employed for multiresolution noise- reduction, thanks to its capability to capture spatial features within frequency subbands. Conversely, Laplacian pyramids (LP) look attractive because of their full band- pass frequency property, which enables connected image structures to be represented on multiple scales. The idea of the present work is to apply an adaptive minimum mean squared error filter to the connectivity-preserving different resolution layers into which the noisy image is decomposed. For natural images, each layer of the LP is characterized by a signal-to-noise ratio (SNR) that decreases for increasing spatial resolution. Therefore, each filter may be tuned to the SNR of the related layer, so as to preserve the spatial details of the less noisy layers to a larger extent. Once all the resolutions, including the base-band, have been adaptively smoothed, a noise-filtered image version is achieved by recombining the layers of the LP. Theoretical frameworks are developed for both additive and multiplicative noise models. Experimental results of de- noising carried out on images with simulated noise and on true synthetic aperture radar images validate the potentiality of the approach in terms of both SNR improvement and visual quality.

关键词： image processing noise filtering edge-preserving smoothing multiresolution analysis Laplacian pyramid adaptive filtering MMSE filtering multiplicative noise speckle Synthetic Aperture Radar images

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applications of time-frequency processing to radar imaging

Applications of time-frequency processing to radar imaging

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wavelet applications iiI conference

作者： Chen, VC USN RES LABDIV RADARWASHINGTONDC 20375

ISBN: (纸本)081942143X

High resolution radar image is always demanded. To achieve high resolution, wideband signal and longer imaging time are required. However, due to time-varying behavior of returned radar signals and due to multiple backscattering behavior of targets, radar image resolution can be significantly degraded and images become blurred. The conventional radar processor uses the Fourier transform to retrieve Doppler information. In order to use the Fourier transform adequately, some restrictions must be applied: the scatterers must remain in their range cells and their Doppler frequency contents should be stationary during the entire imaging time duration. However, due to the target's complex motion, the Doppler frequency contents are actually time-varying. Therefore, the Doppler spectrum obtained from the Fourier transform becomes smeared, and, thus, the resolution of the radar image is degraded. However, the restrictions of the Fourier processing can be lifted if the Doppler information can be retrieved with a method which does not require stationary Doppler spectrum. Therefore, the image blurring caused by the time-varying Doppler spectrum can be resolved without applying sophisticated motion compensation. By replacing the conventional Fourier transform with a time-frequency transform, a 2-D range-Doppler Fourier frame becomes a 3- D time-range-Doppler cube. By sampling in time, a time sequence of 2-D range-Doppler images can be viewed. Each individual time-sampled image from the cube provides superior image resolution and also enhanced signal-to-noise ratio. When the target contains cavities or duct-type structures, these scattering mechanisms appear in radar images as blurred clouds extended in range dimension. It is very useful to combine adaptive time-frequency wavelet transform with the radar imaging technique so that the 'clouds' can be removed and their resonance frequencies can be identified. By applying time-frequency processing for each cross-range lines of radar

关键词： radar imaging adaptive wavelet transform time-frequency processing

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Orthonormal wavelets with balanced uncertainty

Orthonormal wavelets with balanced uncertainty

引用

International conference on image processing (ICIP-96)

作者： Monro, DM Bassil, BE Dickson, GJ UNIV BATH SCH ELECT & ELECT ENGNBATH BA2 7AYAVONENGLAND

ISBN: (纸本)0780332598

This paper addresses the question: `What makes a good wavelet for image compression?', by considering objective and subjective measurements of quality. A new metric is proposed for the design of the Finite Impulse Response (FIR) filters used in the Discrete wavelet Transform (DWT). The metric is the diagonal of the Heisenberg uncertainty rectangle, with time weighted by a factor k relative to frequency. Minimization of the metric balances the time and frequency spreads of the filter response. The metric can be computed directly from the filter coefficients, so it can be used to optimize wavelets for image compression without the cost of repeatedly compressing and decompressing images. A psychovisual evaluation carried out with 24 subjects demonstrates that orthonormal FIR filters designed this way give good subjective results with zerotree image compression. With suitably chosen k, both better subjective quality and lower RMS error are achieved than with wavelets chosen for maximum regularity.

关键词： image compression

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Performance of wavelet packet division multiplexing in impulsive and Gaussian noise channels

Performance of wavelet packet division multiplexing in impul...

引用

4th conference on wavelet applications in signal and image processing

作者： Wu, J Wong, KM Jin, Q Davidson, TN MCMASTER UNIV COMMUN RES LABHAMILTONON L8S 4K1CANADA

ISBN: (纸本)0819422134

wavelet packet division multiplexing (WPDM) is a multiple signal transmission scheme based on wavelet packets. Due to its bandwidth efficiency, channel assignment flexibility, and inherent security features, it has attracted considerable interest. However, before it can be employed as a practical multiplexing technique, the performance of WPDM in various channels has to be fully investigated. In this paper, we explore the system performance when the channel includes impulsive noise as well as Gaussian noise. After briefly reviewing the system model of WPDM, we derive an expression for the probability of error for WPDM in the presence of both impulsive and Gaussian noise. The derivation extends previous work on the performance of digital communication systems when impulsive noise and Gaussian noise are the causes of error to WPDM. For the purpose of comparison, the performance of a TDM system in the same channel environment in also evaluated. The calculations and corresponding simulation results illustrate that WPDM can provide greater immunity to impulsive noise than TDM. This result is intuitively pleasing since one noise burst, which can destroy one bit in TDM, will be spread over all the channels of a WPDM system. In each channel, the spread effects of the impulsive noise may not be strong enough to cause an error.

关键词： wavelet packets multiplexing impulsive noise

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Onboard image compression schemes for Modular Airborne Imaging Spectrometer (MAIS) based on wavelet transform 2

Onboard image compression schemes for Modular Airborne Imagi...

引用

conference on Imaging Spectrometry ii

作者： Zhu, ZY Wang, JY Shanghai Institute of Technical Physics (China)

ISBN: (纸本)081942207X

In this paper, two compression schemes are presented to meet the urgent needs of compressing the huge volume and high data rate of imaging spectrometer(MAIS)'s images. According to the multidimensional feature of the images and the high fidelity requirement of the reconstruction, both schemes were devised to exploit the high redundancy in the both spatial and spectral dimension based on the mature wavelet transform technology. wavelet transform was applied here in two ways: First, with the spatial wavelet transform and the spectral DPCM decorrelation, a ratio up to 84.3 with PSNR > 48db's near-lossless result was attained. This is based on the fact that the edge structure among all the spectral bands are similar while WT has higher resolution in high frequency components. Secondly, with the wavelet's high efficiency in processing the ''Wideband Transient'' signals, it was used to transform the raw nonstationary signals in the spectral dimension. A good result was also attained.

关键词： image compression multidimensional signal processing imaging spectrometer wavelet transform real-time processing

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Orthogonal wavelets for image transmission and compression schemes: Implementation and results

Orthogonal wavelets for image transmission and compression s...

引用

4th conference on wavelet applications in signal and image processing

作者： Ahmadian, A Bharath, AA UNIV LONDON IMPERIAL COLL SCI TECHNOL & MED CTR BIOL & MED SYSTLONDON SW7ENGLAND

ISBN: (纸本)0819422134

Diagnostic quality medical images consume vast amounts of network time, system bandwidth and disk storage in current computer architectures. There are many ways in which the use of system and network resources may be optimize without compromising diagnostic image quality. One of these is in the choice of image representation, both for storage and transfer. In this paper, we show how a particularly flexible method of image representation, based on Mallat's algorithm, leads to efficient methods of both lossy image compression and progressive image transmission. We illustrate the application of a progressive transmission scheme to medical images, and provide some examples of image refinement in a multiscale fashion. We show how thumbnail images created by a multiscale orthogonal decomposition can be optimally interpolated, in a minimum square error sense, based on a generalized Moore-Penrose inverse operator. In the final part of this paper, we show that the representation can provide a framework for lossy image compression, with signal/noise ratios far superior to those provided by a standard JPEG algorithm. The approach can also accommodate precision based progressive coding. We show the results of increasing the priority of encoding a selected region of interest in a bit-stream describing a multiresolution image representation.

关键词： orthogonal wavelets progressive image transmission image compression

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Adaptive pseudo-wavelet algorithms for solving nonlinear partial differential equations

Adaptive pseudo-wavelet algorithms for solving nonlinear par...

引用

4th conference on wavelet applications in signal and image processing

作者： Keiser, JM Beylkin, G UNIV COLORADO PROGRAM APPL MATHBOULDERCO 80309

We numerically solve nonlinear partial differential equations of the form ut equals Lu + Nf(u) where L nd N are linear differential operators and f(u) is a nonlinear function. Equations of this form arise in the mathe... 详细信息

ISBN: (纸本)0819422134

We numerically solve nonlinear partial differential equations of the form u_t equals Lu + Nf(u) where L nd N are linear differential operators and f(u) is a nonlinear function. Equations of this form arise in the mathematical description of a number of phenomena including, for example, signal processing schemes based on solving partial differential equations or integral equations, fluid dynamical problems, and general combustion problems. A generic feature of the solutions of these problems is that they can possess smooth, non-oscillatory and/or shock-like behavior. In our approach we project the solution u(x,t) and the operators L and N into a wavelet basis. The vanishing moments of the basis functions permit a sparse representation of both the solution and operators, which has led us to develop fast, adaptive algorithms for applying operators to functions, e.g. Lu, and computing functions, e.g. f(u) equals u², in the wavelet basis. These algorithms use the fact that wavelet expansions may be viewed as a localized Fourier analysis with multiresolution structure that is automatically adaptive to both smooth and shock-like behavior of the solution. In smooth regions few wavelet coefficients are needed and in singular regions large variations in the solution require more wavelet coefficients. Our new approach allows us to combine many of the desirable features of finite-difference, (pseudo) spectral and front-tracking or adaptive grid methods into a collection of efficient, generic algorithms. It is for this reason that we term our algorithms as adaptive pseudo- wavelet algorithms. We have applied our approach to a number of example problems and present numerical results.

关键词： wavelet bases multiresolution analysis adaptive algorithms numerical methods

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