The proceedings contains 47 papers from the conference of SPIE - wavelets: applications in signal and imageprocessing. The topics discussed include: a class of heavy-tailed multivariate non-Gaussian probability model...
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The proceedings contains 47 papers from the conference of SPIE - wavelets: applications in signal and imageprocessing. The topics discussed include: a class of heavy-tailed multivariate non-Gaussian probability models for wavelet coefficients;iterative projective wavelet methods for denoising;interpolation and denoising of piecewise smooth signals by wavelet regularization;adaptive wavelet thresholding for multichannel signal estimation;resolution enhancement and sampling with wavelets and footprints;multiscale likelihood analysis and image reconstruction;harmonic spline series representation of scaling functions and armlets and balanced multiwavelets.
The proceedings contains 42 papers from the conference of SPIE - wavelets: applications in signals and imageprocessingx - Part two. The topics discussed include: a new local transform without overlaps: a combination...
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The proceedings contains 42 papers from the conference of SPIE - wavelets: applications in signals and imageprocessingx - Part two. The topics discussed include: a new local transform without overlaps: a combination of computational harmonic analysis and PDE;geometric methods for wavelet-based image compression;high-bitrate approximation;new design of orthogonal filter banks using the cayley transform;image registration using threefold orthogonal wavelet;a nonseperable multiwavelet for edge detection;wavelet-based pavement distress detection and evaluation.
This paper explores the application of wavelets to a variety of real life problems and more specifically to imageprocessing problems. A general review of the construction and analysis of wavelet analysis will be pres...
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ISBN:
(纸本)081942840X
This paper explores the application of wavelets to a variety of real life problems and more specifically to imageprocessing problems. A general review of the construction and analysis of wavelet analysis will be presented. The issues like multiresolution analysis in the context of sensor integration and pattern recognition and other salient features of the images using wavelets will be discussed in detail.
A thorough guide to the classical and contemporary mathematical methods of modern signal and imageprocessing Discrete Fourier Analysis and wavelets presents a thorough introduction to the mathematical foundations of ...
ISBN:
(数字)9781118032442
ISBN:
(纸本)9780470294666
A thorough guide to the classical and contemporary mathematical methods of modern signal and imageprocessing Discrete Fourier Analysis and wavelets presents a thorough introduction to the mathematical foundations of signal and imageprocessing. Key concepts and applications are addressed in a thought-provoking manner and are implemented using vector, matrix, and linear algebra methods. With a balanced focus on mathematical theory and computational techniques, this self-contained book equips readers with the essential knowledge needed to transition smoothly from mathematical models to practical digital data applications. The book first establishes a complete vector space and matrix framework for analyzing signals and images. Classical methods such as the discrete Fourier transform, the discrete cosine transform, and their application to JPEG compression are outlined followed by coverage of the Fourier series and the general theory of inner product spaces and orthogonal bases. The book then addresses convolution, filtering, and windowing techniques for signals and images. Finally, modern approaches are introduced, including wavelets and the theory of filter banks as a means of understanding the multiscale localized analysis underlying the JPEG 2000 compression standard. Throughout the book, examples using image compression demonstrate how mathematical theory translates into application. Additional applications such as progressive transmission of images, image denoising, spectrographic analysis, and edge detection are discussed. Each chapter provides a series of exercises as well as a MATLAB project that allows readers to apply mathematical concepts to solving real problems. Additional MATLAB routines are available via the book's related Web site. With its insightful treatment of the underlying mathematics in image compression and signalprocessing, Discrete Fourier Analysis and wavelets is an ideal book for mathematics, engineering, and computer science courses at th
The proceedings contain 47 papers from the conference of SPIE: wavelets-applications in signal and imageprocessing Ix. The topics discussed include: image restoration using statistical wavelet models;noise selection ...
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The proceedings contain 47 papers from the conference of SPIE: wavelets-applications in signal and imageprocessing Ix. The topics discussed include: image restoration using statistical wavelet models;noise selection approach to image restoration;adaptive wavelet lifting for image retrieval;tight frame approximations for gabor and wavelet frames;inverse-constrained projection filters;topological obstructions to localization results and embedding multiresolution spline structures.
Due to the irrational coefficients, the orthogonal wavelet filter banks (FBs) need a lot of resources when implemented on hardware. As a result, there is a decrease in operating speed, a significant memory requirement...
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Due to the irrational coefficients, the orthogonal wavelet filter banks (FBs) need a lot of resources when implemented on hardware. As a result, there is a decrease in operating speed, a significant memory requirement, and an increase in power consumption. This paper proposes an orthogonal low complex symmetric Daubechies-2 (4-tap) wavelet filter bank (FB) to address these problems. This is accomplished by obtaining dyadic filter coefficients and making the suggested FB symmetric by marginally modifying the perfect reconstruction (PR) criterion. By using fewer adders and shifters, the suggested wavelet FB achieves a significant reduction in dynamic power consumption without the need for multipliers. This is confirmed by implementing the suggested wavelet FB on the Zedboard ZYNQ-7000 AP-SoC (Zynq FPGA from xilinx) field programmable gate array (FPGA). The suitability of the suggested FB is tested in medical image retrieval and image compression applications. Results from simulations demonstrate that, when compared to state-of-the-art techniques, the suggested wavelet FB performs better in terms of retrieval accuracy (ARP, ARR) for medical image retrieval and PSNR for image compression on the benchmark image datasets.
Recently, as an emerging signalprocessing technology, the semi-tensor product compressed sensing (STP-CS) has attracted widespread attention in the fields of imageprocessing, communications, and bioinformatics. This...
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Recently, as an emerging signalprocessing technology, the semi-tensor product compressed sensing (STP-CS) has attracted widespread attention in the fields of imageprocessing, communications, and bioinformatics. This article reviews the theoretical foundations, algorithmic designs, and practical applications of STP-CS. It begins by revisiting the basic concepts of compressed sensing (CS) and the definition of the semi-tensor product (STP), followed by a detailed discussion on the theoretical model of STP-CS, optimization of the measurement matrix, and reconstruction algorithms. Furthermore, the article explores the practical applications of STP-CS in areas such as sensor nodes, visual security, image encryption, and spectrum sensing, analyzing its performance advantages and potential challenges in these fields. A comprehensive analysis indicates that STP-CS offers significant benefits in saving storage space, reducing computational complexity, and enhancing data security, making it a promising technology in the field of signalprocessing.
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