This book provides an in-depth, integrated, and up-to-date exposition of the topic of signal decomposition techniques. Application areas of these techniques include speech and image processing, machine vision, information engineering, High-Definition Television, and telecommunications. The book will serve as the major reference for those entering the field, instructors teaching some or all of the topics in an advanced graduate course and researchers needing to consult an authoritative source. n The first book to give a unified and coherent exposition of multiresolutional signal decomposition techniques n Classroom tested textbook clearly describes the commonalities among three key methods-transform coding, and wavelet transforms n Gives comparative performance evaluations of many proposed techniques
The uniqueness of this book is that it covers such important aspects of modern signal processing as block transforms from subband filter banks and wavelet transforms from a common unifying standpoint, thus demonstrating the commonality among these decomposition techniques. In addition, it covers such "hot" areas as signal compression and coding, including particular decomposition techniques and tables listing coefficients of subband and wavelet filters and other important properties. The field of this book (Electrical Engineering/Computer Science) is currently booming, which is, of course, evident from the sales of the previous edition. Since the first edition came out there has been much development, especially as far as the applications. Thus, the second edition addresses new developments in applications-related chapters, especially in chapter 4 "Filterbrook Families: Design and Performance," which is greatly expanded. * Unified and coherent treatment of orthogonal transforms, subbands, and wavelets * Coverage of emerging applications of orthogonal transforms in digital communications and multimedia * Duality between analysis and synthesis filter banks for spectral decomposition and synthesis and analysis transmultiplexer structures * Time-frequency focus on orthogonal decomposition techniques with applications to FDMA, TDMA, and CDMA
The purpose of this thesis is to apply the wavelet transform WT to multiresolution structures for analyzing the information content of images based on multiresolution signal decomposition of the wavelet representation. The advantage of the wavelet transform is the fact that it uses different building blocks than the Fourier's sines and cosines and can also work around any gaps in the data. The wavelet block has start and end points and is a right tool for analyzing nonstationary signals. The wavelet transform is related to wavelets, a scaling function and an input signal. From Haar scaling and wavelets, the wavelet transform system was built by using multiresolution signal decomposition. Since Daubechies' scaling and wavelets contain very unique characteristics, which can compress signals having constant or linear components, they were chosen to build both 1-D and 2-D wavelet transforms. In this thesis, three test signals were carefully selected to be used for comparing the efficiencies of data compression between the wavelet and the Fourier transform. By visually inspecting the results, a wavelet reconstructed signal shows better resolution than the same Fourier reconstructed signal under the same compression ratio. The process of signal decomposition and reconstruction is described as follows: A signal is first broken down into its low and high frequency components. The part that contains the low frequency components contains most of the information, is again decomposed into low and high parts. The coarsest signal is kept in the last stage of the lowpass filter operation. It is obtained through a pyramidal algorithm based on convolutions with quadrature mirror filters. Finally, two specific applications (scaling up and image classification) of wavelet analysis are presented for the case of forested landscapes in the Pacific Northwest, U.S.A. The NMSE (normalized mean square error) is used to quantify the amount of information change with image scaling up. To relate changes in ecological function with changes in ecological pattern and information content which occurs in the process of data compression using the wavelet, a simple classification is performed. Thus, changes in information which occur in scaling-up (i.e. the change in forest pattern which results from filtering using the wavelet) are related to changes in ecological function. It is hoped that the results of the study will contribute to issues concerning data compression using satellite imagery to monitor forest health and develop understanding for scaling problems in ecology.
Signals play an important role in our day-to-day life. We frequently come across signals carrying information in the shape of speech, music, picture and video signals. A signal is a function of independent variables such as time, distance, position, temperature, pressure etc. Main objective of signal processing is concerned with the mathematical representation of signal and the algorithmic operation carried out to extract the information present in it. Application of B-Spline and wavelet tools has been discussed in texture classification in this book. We have introduced a new wavelet decomposition technique using fast recursive generalised IIR filters. We have also proposed Oriented Laplacian Pyramid (OLP) using generalised B- spline filters. This book is mainly useful to students/ researchers who are working in the areas of signal/image processing.
The authors present the W-transform for a multiresolution signal decomposition. One of the differences between the wavelet transform and W-transform is that the W-transform leads to a nonorthogonal signal decomposition. Another difference between the two is the manner in which the W-transform handles the endpoints (boundaries) of the signal. This approach does not restrict the length of the signal to be a power of two. Furthermore, it does not call for the extension of the signal thus, the W-transform is a convenient tool for image compression. They present the basic theory behind the W-transform and include experimental simulations to demonstrate its capabilities.
The concept of a W-matrix is used to give an elementary interpretation of a biorthogonal wavelet decomposition of signals. The authors also give a method to modify the decomposition to give an orthogonal projection on the space spanned by the scaling vectors. Roughly speaking, their treatment is a finite-length analog of the well-known theory of multiresolution analysis of Meyer and Mallat. Their approach differs in that it deals directly with the discrete case, it takes care of the boundary elements without explicit padding, and it uses a notion similar to that of semiorthogonality introduced by Chui. Their algorithm has flexibility in the choice of filter coefficients. The decomposition, orthogonalization, and restoration algorithms are computationally fast.
Multiresolution analysis using the wavelet transform hasreceived considerable attention in recent years by researchers invarious fields. It is a powerful tool for efficiently representingsignals and images at multiple levels of detail with many inherentadvantages, including compression, level-of-detail display,progressive transmission, level-of-detail editing, filtering,modeling, fractals and multifractals, etc. This book aims to provide a simple formalization and new clarity onmultiresolution analysis, rendering accessible obscure techniques,and merging, unifying or completing the technique with encoding,feature extraction, compressive sensing, multifractal analysis andtexture analysis. It is aimed at industrial engineers, medicalresearchers, university lab attendants, lecturer-researchers andresearchers from various specializations. It is also intended tocontribute to the studies of graduate students in engineering,particularly in the fields of medical imaging, intelligentinstrumentation, telecommunications, and signal and imageprocessing. Given the diversity of the problems posed and addressed, this bookpaves the way for the development of new research themes, such asbrain–computer interface (BCI), compressive sensing,functional magnetic resonance imaging (fMRI), tissuecharacterization (bones, skin, etc.) and the analysis of complexphenomena in general. Throughout the chapters, informativeillustrations assist the uninitiated reader in betterconceptualizing certain concepts, taking the form of numerousfigures and recent applications in biomedical engineering,communication, multimedia, finance, etc.
Most existing books on wavelets are either too mathematical or they focus on too narrow a specialty. This book provides a thorough treatment of the subject from an engineering point of view. It is a one-stop source of theory, algorithms, applications, and computer codes related to wavelets. This second edition has been updated by the addition of: a section on "Other Wavelets" that describes curvelets, ridgelets, lifting wavelets, etc a section on lifting algorithms Sections on Edge Detection and Geophysical Applications Section on Multiresolution Time Domain Method (MRTD) and on Inverse problems
Wavelets continue to be powerful mathematical tools that can be used to solve problems for which the Fourier (spectral) method does not perform well or cannot handle. This book is for engineers, applied mathematicians, and other scientists who want to learn about using wavelets to analyze, process, and synthesize images and signals. Applications are described in detail and there are step-by-step instructions about how to construct and apply wavelets. The only mathematically rigorous monograph written by a mathematician specifically for nonspecialists, it describes the basic concepts of these mathematical techniques, outlines the procedures for using them, compares the performance of various approaches, and provides information for problem solving, putting the reader at the forefront of current research.