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International conference on signal processing and Communications

作者： Shivamurti, M. Narasimhan, S. v. Natl Aerosp Labs CSIR Digital Signal Proc & Syst Grp Aerosp Elect & Syst Div Bangalore 560017 Karnataka India

ISBN: (纸本)9781424471379

A new simple and efficient analytic wavelet transform based on Discrete Cosine Harmonic wavelet Transform DCHWT (ADCHWT) has been proposed and is applied for signal and image denoising. The analytic DCHWT has been realized by applying DCHWT to the original signal and its Hilbert transform. The shift invariance and the envelope extraction properties of the ADCHWT have been found to be very effective in denoising compared to that of DCHWT.

关键词： Analytic discrete cosine harmonic wavelet transform Analytic wavelet transform Dual tree complex wavelet transform DCT Shift invariant wavelet transform wavelet transform denoising

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Lossless image compression using wavelets over finite rings and related architectures

Lossless image compression using wavelets over finite rings ...

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conference on wavelet applications in signal and image processing v

作者： Klappenecker, A May, FU Nuckel, A Univ. Karlsruhe (Germany)

ISBN: (纸本)0819425915

In this paper we give a brief introduction to filter banks over commutative rings. In contrast to filter banks over the real numbers, we employ finite ring arithmetic to control the number of bits in the signal representations. This way we avoid the coefficient swell problem that is preeminent in rings of characteristic zero. We derive decompositions for images that are tailored to dedicated hardware implementations. These decompositions reduce the size of line-buffers which dominate the silicon area in integrated circuit implementations. As an application, we derive a lossless compression scheme for 8 bit monochrome images using wavelet filters with values in the ring Z/256Z.

关键词： wavelets commutative rings lossless image compression integrated circuits

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image classification using pseudo power signatures

Image classification using pseudo power signatures

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IEEE International conference on image processing (ICIP 2000)

作者： venkatachalam, v Rice Univ Dept Elect & Comp Engn Comp Math Lab Houston TX 77005 USA

ISBN: (纸本)0780362985

Segmentation and classification are important problems with applications in areas like textural analysis and pattern recognition. This paper describes a single-stage approach to solve the image segmentation/classification problem down to the pixel level, using energy density functions based on the wavelet transform. The energy density functions obtained, called Pseudo Power Signatures, are essentially functions of the scale and orientation, and are obtained using separable approximations to the 2-D wavelet transform. A significant advantage of these representations is that they are invariant to signal magnitude, and spatial location within the: object of interest. Further, they lend themselves to fast and simple classification routines. We provide a complete formulation of the signature determination problem for 2-D, and propose an effective, albeit simple, technique based on a tensor singular value analysis, to solve the problem. We also present an efficient computational algorithm, and a simulation result reflecting the strengths and limitations of this approach.

关键词： image analysis

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wavelet-domain filtering for photon imaging systems

Wavelet-domain filtering for photon imaging systems

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conference on wavelet applications in signal and image processing v

作者： Nowak, R Baraniuk, R Michigan State Univ. (United States) Rice Univ. (United States)

ISBN: (纸本)0819425915

Many imaging systems rely on photon detection as the basis of image formation. One of the major sources of error in these systems is Poisson noise due to the quantum nature of the photon detection process. Unlike additive Gaussian noise, Poisson noise is signal-dependent, and consequently separating signal from noise is a very difficult task. In this paper, we develop a novel wavelet-domain filtering procedure for noise removal in photon imaging systems. The filter adapts to both the signal and the noise and balances the trade-off between noise removal and excessive smoothing of image details. Designed using the statistical method of cross-validation, the filter is simultaneously optimal in a small-sample predictive sum of squares sense and asymptotically optimal in the mean square error sense. The filtering procedure has a simple interpretation as a joint edge detection/estimation process. Moreover, we derive an efficient algorithm for performing the filtering that has the same order of complexity as the fast wavelet transform itself. The performance of the new filter is assessed with simulated data experiments and tested with actual nuclear medicine imagery.

关键词： wavelets photon imaging Poisson processes denoising

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Color image Segmentation via wavelet Frames 4

Color Image Segmentation Via Wavelet Frames

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4th IEEE International conference on signal and image processing (ICSIP)

作者： Yang, Jianbin Zhang, Enlin Wang, Cong Hohai Univ Dept Math Coll Sci Nanjing Jiangsu Peoples R China Xidian Univ Sch Electromech Engn Xian Shaanxi Peoples R China

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.

关键词： wavelet frame image segmentation ADMM algorithm color images

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Iterative block-median pyramid transform-based denoising

Iterative block-median pyramid transform-based denoising

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conference on wavelet applications in signal and image processing vII

作者： Melnik, v Shmulevich, I Egiazarian, K Astola, J Tampere Univ Technol Signal Proc Lab FIN-33101 Tampere Finland

ISBN: (纸本)0819432997

A nonlinear block-median pyramidal transform has been proposed. This transform is based on the iterative application of the median operation and linear Lagrange interpolation. The probability distribution of the transform coefficients has been analytically derived for i.i.d. input signals. The results of this statistical analysis are used for selecting the thresholds for denoising applications. Numerical simulation results are presented.

关键词： multiresolution pyramidal transform denoising nonlinear filter

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Enhanced method for reducing ultrasound speckle noise using wavelet transform

Enhanced method for reducing ultrasound speckle noise using ...

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conference on wavelet applications in signal and image processing v

作者： AbdelMalek, A Dentinger, AM GE Corporate Research and Development Ctr. (United States)

ISBN: (纸本)0819425915

This paper presents the results of the development of an adaptive method for reducing signal-dependent noise, such as speckle noise, in a coherent imaging system signal, such as in medical ultrasound imaging. Speckle noise is filtered using nonlinear adaptive thresholding of received echo wavelet transform coefficients. Filtering speckle noise in ultrasound imaging enhances the resultant image by improving the signal-to-noise ratio. This method includes the steps of transforming the imaging system signal using discrete wavelet transformation to provide wavelet transform coefficients for each of the wavelet scales having different levels of resolution ranging from a finest wavelet scale to a coarsest wavelet scale;deleting the wavelet transform coefficients representing the finest wavelet scale;identifying, for each wavelet scale other than the finest wavelet scale, which of the wavelet transform coefficients are related to noise and which are related to a true signal through the use of adaptive non-linear thresholding;selecting those wavelet transform coefficients which are identified as being related to a true signal;and inverse transforming the selected wavelet transform coefficients using an inverse discrete wavelet transformation to provide an enhanced true signal with reduced noise. This method is shown to improve the signal-to-noise ratio by 2-5 dB in digital ultrasound images of real and phantom objects for a range of thresholding levels while preserving the contrast differences between regions and maintaining feature edges. The filtered images have an enhanced apparent contrast resulting from the reduction in the speckle noise and the preservation of the contrast differences.

关键词： speckle noise reduction wavelets ultrasound

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image processing using wavelet and fractal-based algorithms 5

Image processing using wavelet and fractal-based algorithms

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SPIE conference on wavelet applications v

作者： Siddiqui, KJ SUNY Coll Fredonia Dept Math & Comp Sci Fredonia NY 14063 USA

ISBN: (纸本)081942840X

Modern image and signal processing methods strive to maximize signal to noise ratios, even in the presence of severe noise. Frequently,real world data is degraded by under sampling of intrinsic periodicities, or by sampling with unevenly spaced intervals. This results in dropout or missing data, and such data sets are particularly difficult to process using conventional image processing methods. In many cases, one must still extract as much information as possible from a given data set, although available data may be sparse or noisy. In such cases, we suggest algorithms based on wavelet transform and fractal theory will offer a viable alternative as some early work in the area has indicated. An architecture of a software system is suggested to implement an improved scheme for the analysis, representation, and processing of images. The scheme is based on considering the segments of images as wavelets and fractals so that small details in the images can be exploited and the data can be compressed The objective is to implement this scheme automatically and rapidly decompose a two dimensional image into a combination of elemental images so that an array of processing methods can be applied Thus, the scheme offers potential utility for analysis of images and compression of image data. Moreover, the elemental images could be the patterns that the system is required to recognize, so that the scheme offers potential utility for industrial and military applications involving robot vision and/or automatic recognition of targets.

关键词： discrete wavelet transform (DWT) pattern recognition fractal image analysis feature extraction and selection image interpretation stochastic and independent DWT

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wavelet variance components in image space for spatio-temporal neuroimaging data

Wavelet variance components in image space for spatio-tempor...

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wavelets - applications in signal and image processing X conference

作者： Aston, J Turkheimer, F Cunningham, v Gunn, R US Bur Census Div Stat Res Washington DC 20233 USA

ISBN: (纸本)0819450804

Typical neuroimaging studies place great emphasis on not only the estimation but also the standard error estimates of underlying parameters derived from a temporal model. This is principally done to facilitate the use of t-statistics. Due to the spatial correlations in the data, it can often be more advantageous to interrogate models in the wavelet domain than in the image domain. However, widespread acceptance of these wavelet techniques has been hampered due to the limited ability to generate both parametric and error estimates in the image domain from these temporal models in the wavelet domain, without which comparison to current standard non-wavelet methods can prove difficult.

关键词： wavelets Error analysis Data modeling Functional magnetic resonance imaging Neuroimaging wavelet transforms image analysis Positron emission tomography Statistical analysis Algorithm development

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Multiscale Derivative Transform and Its Application to image Watermarking

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DIGITAL signal processing 2014年 33卷 148-155页

作者： Nezhadarya, Ehsan Ward, Rabab K. Univ British Columbia Dept Elect & Comp Engn Vancouver BC V5Z 1M9 Canada

A transform that estimates the first and higher-order derivatives of images at multiple scales is proposed. The proposed transform, called Multi-Scale Derivative Transform (MSDT), is specially designed for image watermarking applications. To calculate the first and higher-order image derivatives, MSDT uses the detail wavelet coefficients of the image. Unlike traditional wavelet-based image derivative estimators that use only the horizontal and vertical wavelet coefficients, the proposed transform maps the diagonal as well as the horizontal and vertical wavelet coefficients to the horizontal and vertical derivatives of the image. The inverse transform is designed such that any change in the image derivative domain results in the minimum possible change in the wavelet coefficients. This renders a watermark, that is embedded in the derivative domain, less visible in the image domain. The application of this transform to image watermarking is discussed, and the results are compared with those obtained using traditional wavelet-based image derivative estimators. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Derivative transform wavelet transform Watermarking Multi-scale transform Angle quantization index modulation (AQIM)

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