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Relevant bibliographies by topics / Wavelet processing

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Academic literature on the topic 'Wavelet processing'

Author: Grafiati

Published: 28 May 2022

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Journal articles on the topic "Wavelet processing"

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Любимова, Mariya Lyubimova, Князева, and Tatyana Knyazeva. "Processing of tomographic images by means of wavelet analysis." Journal of New Medical Technologies. eJournal 8, no. 1 (November 5, 2014): 1–4. http://dx.doi.org/10.12737/4110.

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The paper is devoted to the problem of processing of tomographic images using wavelet analysis. The features of image processing techniques, indications were analyzed. Wavelets are a signal waveform of limited duration that has an average value of zero. Wavelets are comparable to a sine wave, and they are the basis of Fourier analysis. Wavelet analysis method allows to processing of tomographic images using a large time interval, where more accurate information about the low frequency region and shorter when information is needed on high frequency. The characteristic features of the settings wavelet transforms are described. Their bad choice reduces the reliability of detection of changes in the structure of signals when changing system state. The key stages of the reconstruction tomography images in DICOM format using the method of wavelet analysis were examined; algorithm of noise reduction was investigated. Practical area of application of wavelet analysis doesn´t limited to digital signal processing; it also covers physical experiments, numerical methods and other areas of physics and mathematics. By being able to analyze the non-stationary signals, wavelet analysis has become a powerful alternative Fourier transform in medical applications.

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Zhang, Xi, and Noriaki Fukuda. "Lossy to lossless image coding based on wavelets using a complex allpass filter." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 04 (July 2014): 1460002. http://dx.doi.org/10.1142/s0219691314600029.

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Wavelet-based image coding has been adopted in the international standard JPEG 2000 for its efficiency. It is well-known that the orthogonality and symmetry of wavelets are two important properties for many applications of signal processing and image processing. Both can be simultaneously realized by the wavelet filter banks composed of a complex allpass filter, thus, it is expected to get a better coding performance than the conventional biorthogonal wavelets. This paper proposes an effective implementation of orthonormal symmetric wavelet filter banks composed of a complex allpass filter for lossy to lossless image compression. First, irreversible real-to-real wavelet transforms are realized by implementing a complex allpass filter for lossy image coding. Next, reversible integer-to-integer wavelet transforms are proposed by incorporating the rounding operation into the filtering processing to obtain an invertible complex allpass filter for lossless image coding. Finally, the coding performance of the proposed orthonormal symmetric wavelets is evaluated and compared with the D-9/7 and D-5/3 biorthogonal wavelets. It is shown from the experimental results that the proposed allpass-based orthonormal symmetric wavelets can achieve a better coding performance than the conventional D-9/7 and D-5/3 biorthogonal wavelets both in lossy and lossless coding.

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Denham, John. "WAVELET PROCESSING SEMINAR." Leading Edge 6, no. 2 (February 1987): 30–33. http://dx.doi.org/10.1190/1.1439365.

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Zhang, Fanchang, and Nanying Lan. "Seismic-gather wavelet-stretching correction based on multiwavelet decomposition algorithm." GEOPHYSICS 85, no. 5 (July 10, 2020): V377—V384. http://dx.doi.org/10.1190/geo2018-0835.1.

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Normal moveout correction is crucial in seismic data processing, but it generates a wavelet-stretching effect, especially on the larger offset or incident-angle seismic data. Wavelet stretching reduces the dominant frequency of seismic data. The greater the incident angle or offset, the lower dominant the frequency becomes. This is an unfavorable effect to amplitude variation with offset analysis. Therefore, we have introduced a wavelet stretching correction method based on the multiwavelet decomposition (MWD) algorithm. First, it decomposes the near-offset pilot trace and all the far-offset seismic traces in the same gather into a series of wavelets via the MWD algorithm. Then, the dominant frequencies of wavelets in the far-offset seismic traces are replaced by those corresponding wavelets in the pilot trace. Finally, the wavelets after the stretching correction are used to reconstruct the seismic trace. The model and field-data processing results show that this method can not only effectively reduce the wavelet stretching effect but it can also maintain the amplitude of each wavelet as invariant during the stretching correction procedure. Because only the frequencies of the decomposed wavelets are used, and no inverse wavelet operators is introduced, the wavelet stretching correction method does not distort the amplitude information.

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TODA, HIROSHI, ZHONG ZHANG, and TAKASHI IMAMURA. "PERFECT-TRANSLATION-INVARIANT CUSTOMIZABLE COMPLEX DISCRETE WAVELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 04 (July 2013): 1360003. http://dx.doi.org/10.1142/s0219691313600035.

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The theorems, giving the condition of perfect translation invariance for discrete wavelet transforms, have already been proven. Based on these theorems, the dual-tree complex discrete wavelet transform, the 2-dimensional discrete wavelet transform, the complex wavelet packet transform, the variable-density complex discrete wavelet transform and the real-valued discrete wavelet transform, having perfect translation invariance, were proposed. However, their customizability of wavelets in the frequency domain is limited. In this paper, also based on these theorems, a new type of complex discrete wavelet transform is proposed, which achieves perfect translation invariance with high degree of customizability of wavelets in the frequency domain.

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Low, Yin Fen, and Rosli Besar. "Optimal Wavelet Filters for Medical Image Compression." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 02 (June 2003): 179–97. http://dx.doi.org/10.1142/s0219691303000128.

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Recently, the wavelet transform has emerged as a cutting edge technology, within the field of image compression research. The basis functions of the wavelet transform are known as wavelets. There are a variety of different wavelet functions to suit the needs of different applications. Among the most popular wavelets are Haar, Daubechies, Coiflet and Biorthogonal, etc. The best wavelets (functions) for medical image compression are widely unknown. The purpose of this paper is to examine and compare the difference in impact and quality of a set of wavelet functions (wavelets) to image quality for implementation in a digitized still medical image compression with different modalities. We used two approaches to the measurement of medical image quality: objectively, using peak signal to noise ratio (PSNR) and subjectively, using perceived image quality. Finally, we defined an optimal wavelet filter for each modality of medical image.

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DEBNATH, LOKENATH, and SARALEES NADARAJAH. "POPULAR WAVELET MODELS." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 04 (July 2007): 655–66. http://dx.doi.org/10.1142/s0219691307001951.

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The modern approach for wavelets imposes a Bayesian prior model on the wavelet coefficients to capture the sparseness of the wavelet expansion. The idea is to build flexible probability models for the marginal posterior densities of the wavelet coefficients. In this note, we derive exact expressions for two popular models for the marginal posterior density. We also illustrate the superior performance of these models over the standard models for wavelet coefficients.

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Toda, Hiroshi, Zhong Zhang, and Takashi Imamura. "Practical design of perfect-translation-invariant real-valued discrete wavelet transform." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 04 (July 2014): 1460005. http://dx.doi.org/10.1142/s0219691314600054.

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The real-valued tight wavelet frame having perfect translation invariance (PTI) has already proposed. However, due to the irrational-number distances between wavelets, its calculation amount is very large. In this paper, based on the real-valued tight wavelet frame, a practical design of a real-valued discrete wavelet transform (DWT) having PTI is proposed. In this transform, all the distances between wavelets are multiples of 1/4, and its transform and inverse transform are calculated fast by decomposition and reconstruction algorithms at the sacrifice of a tight wavelet frame. However, the real-valued DWT achieves an approximate tight wavelet frame.

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HUANG, YONGDONG, and ZHENGXING CHENG. "PARAMETRIZATION OF COMPACTLY SUPPORTED TRIVARIATE ORTHOGONAL WAVELET FILTER." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 04 (July 2007): 627–39. http://dx.doi.org/10.1142/s0219691307001938.

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Multivariate wavelets analysis is a powerful tool for multi-dimensional signal processing, but tensor product wavelets have a number of drawbacks. In this paper, we give an algorithm of parametric representation compactly supported trivariate orthogonal wavelet filter, which simplifies the study of trivariate orthogonal wavelet. Four examples are also given to demonstrate the method.

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Sharif, I., and S. Khare. "Comparative Analysis of Haar and Daubechies Wavelet for Hyper Spectral Image Classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 937–41. http://dx.doi.org/10.5194/isprsarchives-xl-8-937-2014.

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With the number of channels in the hundreds instead of in the tens Hyper spectral imagery possesses much richer spectral information than multispectral imagery. The increased dimensionality of such Hyper spectral data provides a challenge to the current technique for analyzing data. Conventional classification methods may not be useful without dimension reduction pre-processing. So dimension reduction has become a significant part of Hyper spectral image processing. This paper presents a comparative analysis of the efficacy of Haar and Daubechies wavelets for dimensionality reduction in achieving image classification. Spectral data reduction using Wavelet Decomposition could be useful because it preserves the distinction among spectral signatures. Daubechies wavelets optimally capture the polynomial trends while Haar wavelet is discontinuous and resembles a step function. The performance of these wavelets are compared in terms of classification accuracy and time complexity. This paper shows that wavelet reduction has more separate classes and yields better or comparable classification accuracy. In the context of the dimensionality reduction algorithm, it is found that the performance of classification of Daubechies wavelets is better as compared to Haar wavelet while Daubechies takes more time compare to Haar wavelet. The experimental results demonstrate the classification system consistently provides over 84% classification accuracy.

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Dissertations / Theses on the topic "Wavelet processing"

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May, Heather. "Wavelet-based Image Processing." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448037498.

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Shi, Fangmin. "Wavelet transforms for stereo imaging." Thesis, University of South Wales, 2002. https://pure.southwales.ac.uk/en/studentthesis/wavelet-transforms-for-stereo-imaging(65abb68f-e30b-4367-a3a8-b7b3df85f566).html.

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Stereo vision is a means of obtaining three-dimensional information by considering the same scene from two different positions. Stereo correspondence has long been and will continue to be the active research topic in computer vision. The requirement of dense disparity map output is great demand motivated by modern applications of stereo such as three-dimensional high-resolution object reconstruction and view synthesis, which require disparity estimates in all image regions. Stereo correspondence algorithms usually require significant computation. The challenges are computational economy, accuracy and robustness. While a large number of algorithms for stereo matching have been developed, there still leaves the space for improvement especially when a new mathematical tool such as wavelet analysis becomes mature. The aim of the thesis is to investigate the stereo matching approach using wavelet transform with a view to producing efficient and dense disparity map outputs. After the shift invariance property of various wavelet transforms is identified, the main contributions of the thesis are made in developing and evaluating two wavelet approaches (the dyadic wavelet transform and complex wavelet transform) for solving the standard correspondence problem. This comprises an analysis of the applicability of dyadic wavelet transform to disparity map computation, the definition of a waveletbased similarity measure for matching, the combination of matching results from different scales based on the detectable minimum disparity at each scale and the application of complex wavelet transform to stereo matching. The matching method using the dyadic wavelet transform is through SSD correlation comparison and is in particular detailed. A new measure using wavelet coefficients is defined for similarity comparison. The approach applying a dual tree of complex wavelet transform to stereo matching is formulated through phase information. A multiscale matching scheme is applied for both the matching methods. Imaging testing has been made with various synthesised and real image pairs. Experimental results with a variety of stereo image pairs exhibit a good agreement with ground truth data, where available, and are qualitatively similar to published results for other stereo matching approaches. Comparative results show that the dyadic wavelet transform-based matching method is superior in most cases to the other approaches considered.

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Choe, Gwangwoo. "Merged arithmetic for wavelet transforms /." Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p3004235.

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Masud, Shahid. "VLSI systems for discrete wavelet transforms." Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300782.

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Silwal, Sharad Deep. "Bayesian inference and wavelet methods in image processing." Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/2355.

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Silva, Eduardo Antonio Barros da. "Wavelet transforms for image coding." Thesis, University of Essex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282495.

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Wu, Jiangfeng. "Wavelet packet division multiplexing." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0002/NQ42889.pdf.

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Long, Christopher J. "Wavelet methods in speech recognition." Thesis, Loughborough University, 1999. https://dspace.lboro.ac.uk/2134/14108.

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In this thesis, novel wavelet techniques are developed to improve parametrization of speech signals prior to classification. It is shown that non-linear operations carried out in the wavelet domain improve the performance of a speech classifier and consistently outperform classical Fourier methods. This is because of the localised nature of the wavelet, which captures correspondingly well-localised time-frequency features within the speech signal. Furthermore, by taking advantage of the approximation ability of wavelets, efficient representation of the non-stationarity inherent in speech can be achieved in a relatively small number of expansion coefficients. This is an attractive option when faced with the so-called 'Curse of Dimensionality' problem of multivariate classifiers such as Linear Discriminant Analysis (LDA) or Artificial Neural Networks (ANNs). Conventional time-frequency analysis methods such as the Discrete Fourier Transform either miss irregular signal structures and transients due to spectral smearing or require a large number of coefficients to represent such characteristics efficiently. Wavelet theory offers an alternative insight in the representation of these types of signals. As an extension to the standard wavelet transform, adaptive libraries of wavelet and cosine packets are introduced which increase the flexibility of the transform. This approach is observed to be yet more suitable for the highly variable nature of speech signals in that it results in a time-frequency sampled grid that is well adapted to irregularities and transients. They result in a corresponding reduction in the misclassification rate of the recognition system. However, this is necessarily at the expense of added computing time. Finally, a framework based on adaptive time-frequency libraries is developed which invokes the final classifier to choose the nature of the resolution for a given classification problem. The classifier then performs dimensionaIity reduction on the transformed signal by choosing the top few features based on their discriminant power. This approach is compared and contrasted to an existing discriminant wavelet feature extractor. The overall conclusions of the thesis are that wavelets and their relatives are capable of extracting useful features for speech classification problems. The use of adaptive wavelet transforms provides the flexibility within which powerful feature extractors can be designed for these types of application.

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Cena, Bernard Maria. "Reconstruction for visualisation of discrete data fields using wavelet signal processing." University of Western Australia. Dept. of Computer Science, 2000. http://theses.library.uwa.edu.au/adt-WU2003.0014.

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The reconstruction of a function and its derivative from a set of measured samples is a fundamental operation in visualisation. Multiresolution techniques, such as wavelet signal processing, are instrumental in improving the performance and algorithm design for data analysis, filtering and processing. This dissertation explores the possibilities of combining traditional multiresolution analysis and processing features of wavelets with the design of appropriate filters for reconstruction of sampled data. On the one hand, a multiresolution system allows data feature detection, analysis and filtering. Wavelets have already been proven successful in these tasks. On the other hand, a choice of discrete filter which converges to a continuous basis function under iteration permits efficient and accurate function representation by providing a “bridge” from the discrete to the continuous. A function representation method capable of both multiresolution analysis and accurate reconstruction of the underlying measured function would make a valuable tool for scientific visualisation. The aim of this dissertation is not to try to outperform existing filters designed specifically for reconstruction of sampled functions. The goal is to design a wavelet filter family which, while retaining properties necessary to preform multiresolution analysis, possesses features to enable the wavelets to be used as efficient and accurate “building blocks” for function representation. The application to visualisation is used as a means of practical demonstration of the results. Wavelet and visualisation filter design is analysed in the first part of this dissertation and a list of wavelet filter design criteria for visualisation is collated. Candidate wavelet filters are constructed based on a parameter space search of the BC-spline family and direct solution of equations describing filter properties. Further, a biorthogonal wavelet filter family is constructed based on point and average interpolating subdivision and using the lifting scheme. The main feature of these filters is their ability to reconstruct arbitrary degree piecewise polynomial functions and their derivatives using measured samples as direct input into a wavelet transform. The lifting scheme provides an intuitive, interval-adapted, time-domain filter and transform construction method. A generalised factorisation for arbitrary primal and dual order point and average interpolating filters is a result of the lifting construction. The proposed visualisation filter family is analysed quantitatively and qualitatively in the final part of the dissertation. Results from wavelet theory are used in the analysis which allow comparisons among wavelet filter families and between wavelets and filters designed specifically for reconstruction for visualisation. Lastly, the performance of the constructed wavelet filters is demonstrated in the visualisation context. One-dimensional signals are used to illustrate reconstruction performance of the wavelet filter family from noiseless and noisy samples in comparison to other wavelet filters and dedicated visualisation filters. The proposed wavelet filters converge to basis functions capable of reproducing functions that can be represented locally by arbitrary order piecewise polynomials. They are interpolating, smooth and provide asymptotically optimal reconstruction in the case when samples are used directly as wavelet coefficients. The reconstruction performance of the proposed wavelet filter family approaches that of continuous spatial domain filters designed specifically for reconstruction for visualisation. This is achieved in addition to retaining multiresolution analysis and processing properties of wavelets.

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Anton, Wirén. "The Discrete Wavelet Transform." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55063.

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In this thesis we will explore the theory behind wavelets. The main focus is on the discrete wavelet transform, although to reach this goal we will also introduce the discrete Fourier transform as it allow us to derive important properties related to wavelet theory, such as the multiresolution analysis. Based on the multiresolution it will be shown how the discrete wavelet transform can be formulated and show how it can be expressed in terms of a matrix. In later chapters we will see how the discrete wavelet transform can be generalized into two dimensions, and discover how it can be used in image processing.

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Books on the topic "Wavelet processing"

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Chan, Y. T. Wavelet basics. Boston: Kluwer Academic Publishers, 1995.

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Suter, Bruce W. Multirate and wavelet signal processing. San Diego: Acadmeic Press, 1998.

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A, Gopinath Ramesh, and Guo Haitao, eds. Introduction to wavelets and wavelet transforms: A primer. Upper Saddle River, N.J: Prentice Hall, 1998.

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Oberti, Frederic. Image processing using parallel processing methods: Wavelet approach. Manchester: UMIST, 1997.

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A wavelet tour of signal processing. San Diego: Academic Press, 1998.

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A wavelet tour of signal processing. 2nd ed. San Diego: Academic Press, 1999.

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Mallat, Stéphane. A wavelet tour of signal processing. 2nd ed. San Diego: Academic Press, 1999.

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Discrete wavelet transform: A signal processing approach. Chichester, UK: John Wiley & Sons, 2015.

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Prasad, L. Wavelet analysis with applications to image processing. Boca Raton: CRC Press, 1997.

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Wavelet transforms and localization operators. Basel: Birkhäuser Verlag, 2002.

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Book chapters on the topic "Wavelet processing"

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Teolis, Anthony. "Wavelet Signal Processing." In Modern Birkhäuser Classics, 171–261. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65747-9_7.

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Teolis, Anthony. "Wavelet Signal Processing." In Computational Signal Processing with Wavelets, 171–261. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4142-3_7.

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Rao, K. Deergha, and M. N. S. Swamy. "Discrete Wavelet Transforms." In Digital Signal Processing, 619–91. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8081-4_10.

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Semmlow, John L., and Benjamin Griffel. "Wavelet Analysis." In BIOSIGNAL and MEDICAL IMAGE PROCESSING, 217–46. 3rd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/b16584-7.

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Coifman, R. R. "Wavelet Analysis and Signal Processing." In Signal Processing, 59–68. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4684-6393-4_5.

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Brémaud, Pierre. "The Wavelet Transform." In Mathematical Principles of Signal Processing, 185–94. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3669-4_13.

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Brémaud, Pierre. "Wavelet Orthonormal Expansions." In Mathematical Principles of Signal Processing, 195–215. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3669-4_14.

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Claude, Francisco, and Gonzalo Navarro. "The Wavelet Matrix." In String Processing and Information Retrieval, 167–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34109-0_18.

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Cohen, Albert, and Robert D. Ryan. "Biorthogonal wavelet bases." In Wavelets and Multiscale Signal Processing, 97–163. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4425-2_5.

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Teolis, Anthony. "Discrete Wavelet Transform." In Computational Signal Processing with Wavelets, 89–126. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4142-3_5.

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Conference papers on the topic "Wavelet processing"

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Ben-Ezra, Y., and B. I. Lembrikov. "Optical wavelet signal processing." In 2009 11th International Conference on Transparent Optical Networks (ICTON). IEEE, 2009. http://dx.doi.org/10.1109/icton.2009.5185182.

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Fernandes, Felix, Rutger van Spaendonck, Mark J. Coates, and C. Sidney Burrus. "Directional complex-wavelet processing." In International Symposium on Optical Science and Technology, edited by Akram Aldroubi, Andrew F. Laine, and Michael A. Unser. SPIE, 2000. http://dx.doi.org/10.1117/12.408642.

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Teolis, Anthony, and John S. Baras. "Wavelet processing workstation: an interactive MATLAB-based computational tool for wavelet processing." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Harold H. Szu. SPIE, 1995. http://dx.doi.org/10.1117/12.205422.

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Hart, Douglas I., Bruce W. Hootman, and Alexander R. Jackson. "Modeling the seismic wavelet using model‐based wavelet processing." In SEG Technical Program Expanded Abstracts 2001. Society of Exploration Geophysicists, 2001. http://dx.doi.org/10.1190/1.1816484.

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Claypoole, Jr., Roger L., Richard G. Baraniuk, and Robert D. Nowak. "Lifting for nonlinear wavelet processing." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Michael A. Unser, Akram Aldroubi, and Andrew F. Laine. SPIE, 1999. http://dx.doi.org/10.1117/12.366794.

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You, Sihai, Hongli Wang, Lei Feng, Yiyang He, Qiang Xu, and YongQiang Xiao. "Lifting wavelet denoising based on pulsar wavelet basis." In Eleventh International Conference on Signal Processing Systems, edited by Kezhi Mao. SPIE, 2019. http://dx.doi.org/10.1117/12.2559602.

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Martinek, Radek, Rene Jaros, Petr Bilik, Jana Lancova, Marcel Fajkus, and Jan Nedoma. "Ballistocardiography Signal Processing by Wavelet Transform." In 2018 International Conference on Intelligent Systems (IS). IEEE, 2018. http://dx.doi.org/10.1109/is.2018.8710478.

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Szecowka, P. M., M. Kowalski, K. Krysztoforski, and A. R. Wolczowski. "Wavelet Processing Implementation in Digital Hardware." In 2007 14th International Conference on Mixed Design of Integrated Circuits and Systems. IEEE, 2007. http://dx.doi.org/10.1109/mixdes.2007.4286243.

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Laine, Andrew F., Sergio Schuler, Walter Huda, Janice C. Honeyman-Buck, and Barbara G. Steinbach. "Hexagonal wavelet processing of digital mammography." In Medical Imaging 1993, edited by Murray H. Loew. SPIE, 1993. http://dx.doi.org/10.1117/12.154543.

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Diou, C., L. Torres, and M. Robert. "A wavelet core for video processing." In Proceedings of 7th IEEE International Conference on Image Processing. IEEE, 2000. http://dx.doi.org/10.1109/icip.2000.899406.

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Reports on the topic "Wavelet processing"

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Wilson, Gary R. Wavelet Based Cumulant Processing. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada272003.

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Jagler, Karl B. Wavelet Signal Processing for Transient Feature Extraction. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada250519.

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DeVore, Ronald A. Advanced Wavelet Methods for Image and Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada417316.

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Hippenstiel, Ralph D., Monique P. Fargues, Nabil H. Khalil, and Howard F. Overdyk. Processing of Second Order Statistics via Wavelet Transforms. Fort Belvoir, VA: Defense Technical Information Center, February 1998. http://dx.doi.org/10.21236/ada339331.

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Ioup, Juliette W., George E. Ioup, and Joseph S. Wheatley. Wavelet Digital Signal Processing of Undersea Acoustic Data. Fort Belvoir, VA: Defense Technical Information Center, April 2002. http://dx.doi.org/10.21236/ada405774.

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Potes, Cristhian, Ricardo Von Borries, and Cristiano J. Miosso. Wavelet-Based Signal Processing for Monitoring Discomfort and Fatigue. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada494155.

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Sherlock, Barry G. Wavelet-Based Signal and Image Processing for Target Recognition. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada409223.

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Drumheller, David M. Theory and Application of the Wavelet Transform to Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada239533.

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DeVore, Ronald A., and Pencho Petrushev. Highly Nonlinear Algorithms for Wavelet Based Image Processing With Military Applications. Fort Belvoir, VA: Defense Technical Information Center, February 2001. http://dx.doi.org/10.21236/ada391704.

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Lojzim, Joshua Michael, and Marcus Fries. Brain Tumor Segmentation Using Morphological Processing and the Discrete Wavelet Transform. Journal of Young Investigators, August 2017. http://dx.doi.org/10.22186/jyi.33.3.55-62.

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