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Wavelet Domain (wavelet + domain)
Selected AbstractsNoise removal for medical X-ray images in wavelet domainELECTRICAL ENGINEERING IN JAPAN, Issue 3 2008Ling Wang Abstract Many important problems in engineering and science are well-modeled by Poisson noise, and the noise of medical X-ray images is Poisson noise. In this paper, we propose a method for noise removal for degraded medical X-ray images using improved preprocessing and an improved BayesShrink (IBS) method in the wavelet domain. First, we preprocess the medical X-ray image. Second, we apply the Daubechies (db) wavelet transform to medical X-ray images to acquire scaling and wavelet coefficients. Third, we apply the proposed IBS method to process wavelet coefficients. Finally, we compute the inverse wavelet transform for the threshold coefficients. Experimental results show that the proposed method always outperforms traditional methods. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(3): 37, 46, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20486 [source] Pattern recognition in capillary electrophoresis data using dynamic programming in the wavelet domainELECTROPHORESIS, Issue 13 2008Gerardo A. Ceballos Abstract A novel approach for CE data analysis based on pattern recognition techniques in the wavelet domain is presented. Low-resolution, denoised electropherograms are obtained by applying several preprocessing algorithms including denoising, baseline correction, and detection of the region of interest in the wavelet domain. The resultant signals are mapped into character sequences using first derivative information and multilevel peak height quantization. Next, a local alignment algorithm is applied on the coded sequences for peak pattern recognition. We also propose 2-D and 3-D representations of the found patterns for fast visual evaluation of the variability of chemical substances concentration in the analyzed samples. The proposed approach is tested on the analysis of intracerebral microdialysate data obtained by CE and LIF detection, achieving a correct detection rate of about 85% with a processing time of less than 0.3,s per 25,000-point electropherogram. Using a local alignment algorithm on low-resolution denoised electropherograms might have a great impact on high-throughput CE since the proposed methodology will substitute automatic fast pattern recognition analysis for slow, human based time-consuming visual pattern recognition methods. [source] Image coding based on wavelet feature vectorINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 2 2005Shinfeng D. Lin Abstract In this article, an efficient image coding scheme that takes advantages of feature vector in wavelet domain is proposed. First, a multi-stage discrete wavelet transform is applied on the image. Then, the wavelet feature vectors are extracted from the wavelet-decomposed subimages by collecting the corresponding wavelet coefficients. And finally, the image is coded into bit-stream by applying vector quantization (VQ) on the extracted wavelet feature vectors. In the encoder, the wavelet feature vectors are encoded with a codebook where the dimension of codeword is less than that of wavelet feature vector. By this way, the coding system can greatly improve its efficiency. However, to fully reconstruct the image, the received indexes in the decoder are decoded with a codebook where the dimension of codeword is the same as that of wavelet feature vector. Therefore, the quality of reconstructed images can be preserved well. The proposed scheme achieves good compression efficiency by the following three methods. (1) Using the correlation among wavelet coefficients. (2) Placing different emphasis on wavelet coefficients at different decomposing levels. (3) Preserving the most important information of the image by coding the lowest-pass subimage individually. In our experiments, simulation results show that the proposed scheme outperforms the recent VQ-based image coding schemes and wavelet-based image coding techniques, respectively. Moreover, the proposed scheme is also suitable for very low bit rate image coding. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 123,130, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20045 [source] Wavelet-based adaptive vector quantization for still-image codingINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 4 2002Wen-Shiung Chen Abstract Wavelet transform coding (WTC) with vector quantization (VQ) has been shown to be efficient in the application of image compression. An adaptive vector quantization coding scheme with the Gold-Washing dynamic codebook-refining mechanism in the wavelet domain, called symmetric wavelet transform-based adaptive vector quantization (SWT-GW-AVQ), is proposed for still-image coding in this article. The experimental results show that the GW codebook-refining mechanism working in the wavelet domain rather than the spatial domain is very efficient, and the SWT-GW-AVQ coding scheme may improve the peak signal-to-noise ratio (PSNR) of the reconstructed images with a lower encoding time. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 166,174, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10024 [source] Accelerating the analyses of 3-way and 4-way PARAFAC models utilizing multi-dimensional wavelet compressionJOURNAL OF CHEMOMETRICS, Issue 11-12 2005Jeff Cramer Abstract Parallel factor analysis (PARAFAC) is one of the most popular methods for evaluating multi-way data sets, such as those typically acquired by hyphenated measurement techniques. One of the reasons for PARAFAC popularity is the ability to extract directly interpretable chemometric models with little a priori information and the capability to handle unknown interferents and missing values. However, PARAFAC requires long computation times that often prohibit sufficiently fast analyses for applications such as online sensing. An additional challenge faced by PARAFAC users is the handling and storage of very large, high-dimensional data sets. Accelerating computations and reducing storage requirements in multi-way analyses are the topics of this manuscript. This study introduces a data pre-processing method based on multi-dimensional wavelet transforms (WTs), which enables highly efficient data compression applied prior to data evaluation. Because multi-dimensional WTs are linear, the intrinsic underlying linear data construction is preserved in the wavelet domain. In almost all studied examples, computation times for analyzing the much smaller, compressed data sets could be reduced so much that the additional effort for wavelet compression was more than recompensated. For 3-way and 4-way synthetic and experimental data sets, acceleration factors up to 50 have been achieved; these data sets could be compressed down to a few per cent of the original size. Despite the high compression, accurate and interpretable models were derived, which are in good agreement with conventionally determined PARAFAC models. This study also found that the wavelet type used for compression is an important factor determining acceleration factors, data compression ratios and model quality. Copyright © 2006 John Wiley & Sons, Ltd. [source] Compressed sensing in hyperpolarized 3He Lung MRIMAGNETIC RESONANCE IN MEDICINE, Issue 4 2010Salma Ajraoui Abstract In this work, the application of compressed sensing techniques to the acquisition and reconstruction of hyperpolarized 3He lung MR images was investigated. The sparsity of 3He lung images in the wavelet domain was investigated through simulations based on fully sampled Cartesian two-dimensional and three-dimensional 3He lung ventilation images, and the k -spaces of 2D and 3D images were undersampled randomly and reconstructed by minimizing the L1 norm. The simulation results show that temporal resolution can be readily improved by a factor of 2 for two-dimensional and 4 to 5 for three-dimensional ventilation imaging with 3He with the levels of signal to noise ratio (SNR) (,19) typically obtained. The feasibility of producing accurate functional apparent diffusion coefficient (ADC) maps from undersampled data acquired with fewer radiofrequency pulses was also demonstrated, with the preservation of quantitative information (mean ADCcs , mean ADCfull , 0.16 cm2 sec,1). Prospective acquisition of 2-fold undersampled two-dimensional 3He images with a compressed sensing k -space pattern was then demonstrated in a healthy volunteer, and the results were compared to the equivalent fully sampled images (SNRcs = 34, SNRfull = 19). Magn Reson Med 63:1059,1069, 2010. © 2010 Wiley-Liss, Inc. [source] Exploiting statistical properties of wavelet coefficient for face detection and recognitionPROCEEDINGS IN APPLIED MATHEMATICS & MECHANICS, Issue 1 2007Naseer Al-Jawad Wavelet transforms (WT) are widely accepted as an essential tool for image processing and analysis. Image and video compression, image watermarking, content-base image retrieval, face recognition, texture analysis, and image feature extraction are all but few examples. It provides an alternative tool for short time analysis of quasi-stationary signals, such as speech and image signals, in contrast to the traditional short-time Fourier transform. The Discrete Wavelet Transform (DWT) is a special case of the WT, which provides a compact representation of a signal in the time and frequency domain. In particular, wavelet transforms are capable of representing smooth patterns as well anomalies (e.g. edges and sharp corners) in images. We are focusing here on using wavelet transforms statistical properties for facial feature detection, which allows us to extract the image facial feature/edges easily. Wavelet sub-bands segmentation method been developed and used to clean up the non-significant wavelet coefficients in wavelet sub-band (k) based on the (k-1) sub-band. Moreover, erosion which is considered as one of the fundamental operation in morphological image processing, been used to reduce the unwanted edges in certain directions. For face detection, face template profiles been built for both the face and the eyes for different wavelet sub-band levels to achieve better computational performance, these profiles used to match the extracted profiles from the wavelet domain of the input image using the Dynamic Time Warping technique DTW. The DTW smallest distance allows identifying the face and the eyes location. The performance of face features distances and ratio has been also tested for face verification purposes. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Time adaptive denoising of single trial event- related potentials in the wavelet domainPSYCHOPHYSIOLOGY, Issue 6 2000Arndt Effern We present a new wavelet-based method for single trial analysis of transient and time variant event-related potentials (ERPs). Expecting more accurate filter settings than achieved by other techniques (low-pass filter, a posteriori Wiener filter, time invariant wavelet filter), ERPs were initially balanced in time. By simulation, better filter performance could be established for test signals contaminated with either white noise or isospectral noise. To provide an example of real application, the method was applied to limbic P300 potentials (MTL-P300). As a result, variance of single trial MTL-P300s decreased, without restricting the corresponding mean. The proposed method can be regarded as an alternative for single-trial ERP analysis. [source] Inducing normality from non-Gaussian long memory time series and its application to stock return dataAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2010Kyungduk Ko Abstract Motivated by Lee and Ko (Appl. Stochastic Models. Bus. Ind. 2007; 23:493,502) but not limited to the study, this paper proposes a wavelet-based Bayesian power transformation procedure through the well-known Box,Cox transformation to induce normality from non-Gaussian long memory processes. We consider power transformations of non-Gaussian long memory time series under the assumption of an unknown transformation parameter, a situation that arises commonly in practice, while most research has been devoted to non-linear transformations of Gaussian long memory time series with known transformation parameter. Specially, this study is mainly focused on the simultaneous estimation of the transformation parameter and long memory parameter. To this end, posterior estimations via Markov chain Monte Carlo methods are performed in the wavelet domain. Performances are assessed on a simulation study and a German stock return data set. Copyright © 2009 John Wiley & Sons, Ltd. [source] A wavelet solution to the spurious regression of fractionally differenced processesAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2003Yanqin Fan Abstract In this paper we propose to overcome the problem of spurious regression between fractionally differenced processes by applying the discrete wavelet transform (DWT) to both processes and then estimating the regression in the wavelet domain. The DWT is known to approximately decorrelate heavily autocorrelated processes and, unlike applying a first difference filter, involves a recursive two-step filtering and downsampling procedure. We prove the asymptotic normality of the proposed estimator and demonstrate via simulation its efficacy in finite samples. Copyright © 2003 John Wiley & Sons, Ltd. [source] | |||||||||||||